1368 Research application/design study H. Jiang, S. Shi, S. Zhang, J. Zheng
and Q. Li (corresponding author) are with School of Information Science and
Technology, ShanghaiTech University, and Shanghai Engineering Research Center
of Intelligent Vision and Imaging, China. E-mail: {jianghr2023, shishh2023,
zhangshh, zhengjie<EMAIL_ADDRESS>
# SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System
for GNN-based Synthetic Lethal Prediction

Haoran Jiang0009-0009-5717-4208  Shaohan Shi0009-0004-3384-8304  Shuhao
Zhang0009-0008-1933-1869  Jie Zheng0000-0001-6774-9786 and Quan
Li0000-0003-2249-0728

###### Abstract

Synthetic Lethal (SL) relationships, though rare among the vast array of gene
combinations, hold substantial promise for targeted cancer therapy. Despite
advancements in AI model accuracy, there is still a significant need among
domain experts for interpretive paths and mechanism explorations that align
better with domain-specific knowledge, particularly due to the high costs of
experimentation. To address this gap, we propose an iterative Human-AI
collaborative framework with two key components: 1) Human-Engaged Knowledge
Graph Refinement based on Metapath Strategies, which leverages insights from
interpretive paths and domain expertise to refine the knowledge graph through
metapath strategies with appropriate granularity. 2) Cross-Granularity SL
Interpretation Enhancement and Mechanism Analysis, which aids experts in
organizing and comparing predictions and interpretive paths across different
granularities, uncovering new SL relationships, enhancing result
interpretation, and elucidating potential mechanisms inferred by Graph Neural
Network (GNN) models. These components cyclically optimize model predictions
and mechanism explorations, enhancing expert involvement and intervention to
build trust. Facilitated by SLInterpreter, this framework ensures that newly
generated interpretive paths increasingly align with domain knowledge and
adhere more closely to real-world biological principles through iterative
Human-AI collaboration. We evaluate the framework’s efficacy through a case
study and expert interviews.

###### keywords:

Synthetic Lethality, Model Interpretability, Visual Analytics, Iterative
Human-AI Collaboration.

In Case Study, the biologist’s analysis comprises: 1 Select the Thyroid Cancer
in the Disease search box. 2 Observe the similar pattern between KRAS and CDK1
by zooming in on the Radar in the Embedding View. 3 Select CDK1 in the Primary
Gene search box. 4 Select MYC, a correctly predicted partner gene of CDK1, in
Partner Gene Table. 5 MYC tagged in Partner Gene search box. 6 The Entity Flow
Bar indicates that gene-gene connections are no longer predominant. 7 The Path
Bar displays a smaller ratio of SL_GsG. 8 Narrow the investigation scope to
CDK1$\rightarrow$Gene$\rightarrow$BP by color encoding in the Knowledge Graph.
9 Select the irrational path in the Knowledge Graph. 10 Observe the histogram
of sensory_perception_of_smell, indicating that this entity often ends such
metapaths. 11 Select L-H-L strategy by clicking on All edge. 12 Observe that
the proportional relationship of L-H-L is not statistically significant within
its parent set H-H-L. 13 Select H-H-L strategy by clicking on Gene. 14 Observe
the proportional relationships of L-H-L and H-L-L, sub-strategies to H-H-L. 15
Add the formulated strategy to Operation List and enter memos. 16 Click  to
retrain the model. 17 Select the retrained model to initiate an iterative
exploration. 18 Identify the cluster of three similarly ranked genes, EGFR,
MYC, and RPL13, using the Rank Indicator. 19 Lasso the three clustered genes.

Introduction

Synthetic Lethal (SL) relationships among genes denote a specific type of gene
interaction characterized by co-expression [21]. In this context, inhibiting
one lethal gene alone does not affect cell survival, but simultaneous
inhibition of both genes results in cell death [12], as depicted in Fig. 1.
This concept is crucial for targeted cancer therapy, as targeting the SL
partner gene of a known cancer-related gene allows for precise cancer cell
elimination while sparing normal cells. Thus, it offers a viable strategy for
inducing the demise of cancer cells when directly targeting certain genes is
challenging.

Given the substantial medical relevance of SL pairs, screening them is crucial
for clinical applications. Several experimental biology-based methods, such as
RNA interference and genome editing with CRISPR/Cas9 [41], have been
introduced for comprehensive SL screening [59]. However, these traditional wet
lab methods are resource and labor-intensive, time-consuming, and prone to
off-target effects [17]. Additionally, validated SL pairs constitute less than
0.1% of all potential pairs due to the vast number of human gene combinations
[4]. To overcome these challenges, computational methods [49], based on
biological insights, have been suggested to speed up and enhance the precision
of SL gene pair predictions. These methods span statistical approaches [9],
network-based strategies [15], and classic machine learning (ML) [56], along
with artificial intelligence (AI) techniques [36]. Notably, AI methods have
demonstrated superior predictive accuracy. However, aside from achieving
accurate predictions, biologists increasingly prioritize understanding the
interpretation of SL relationship mechanisms implicit in these AI models.
Nevertheless, grasping the biological mechanisms underlying the predicted
results remains challenging [46]. Moreover, the current AI model tools
generally lack sufficient interpretability, resulting in a skeptical stance
among researchers toward the predictive outcomes in this field.

Figure 1: SL gene pairs refer to a pair of genes where inhibiting one does not
impact cancer cell survival, but inhibiting both leads to cell death.

Prior research [52, 64, 50, 13] has aimed to aid biologists in comprehending
the behavior of prediction models, facilitating the exploration of mechanisms
behind successful predictions. These approaches are often rooted in biological
knowledge. For instance, KG4SL[52] enhanced SL prediction accuracy by directly
incorporating various relevant biological factors into the Knowledge Graph
(KG) and integrating them with Graph Neural Networks (GNN). Going a step
further, KR4SL [64] developed an interpretable AI model for SL prediction.
Expanding the scope, efforts have been made to support both domain experts and
non-AI-experts in exploring and explaining model behaviors [50, 13].

Despite aiding target users in understanding parts of model behaviors to
enhance interpretability, the methods mentioned above bring new challenges to
Human-AI collaboration, which stem from two intertwined dimensions: the
symbiotic interplay between biologists’ involvement and the application of AI.
On one hand, earlier research has mainly aimed at improving prediction
accuracy by incorporating vast amounts of information into the model input
[29]. However, in efforts to understand SL relationship mechanisms, the
complex data used for model input significantly increases the complexity of
processing and analysis. Given the intricacies of biological data, some gene
or protein interactions might have subtle relevance to SL mechanisms. This
subtlety potentially introduces new noise, influencing both prediction
outcomes [67] and interpretive subgraphs [24]. Hence, a method synergizing
with domain expertise becomes crucial to effectively devise reasonable
strategies for screening biological knowledge used to train AI models. On the
other hand, biologists seek to understand AI models and expect clear insights
into predictions. This comprehension is vital for decision-making and
selecting wet lab directions, allowing experts to trust or work with these
models [16]. Within this collaborative process, biologists must carefully
consider the division of responsibilities between themselves and the AI models
while simultaneously evaluating their reliability. In the end, biologists bear
the accountability for prediction outcomes. Current approaches mainly provide
AI-generated suggestions for biologists to make final decisions [64], with
little focus on the iterative synergy between AI and biologists, such as
effective means to rectify the misleading knowledge acquired by the model.
Nevertheless, an iterative interaction [57] has the potential to significantly
impact the quality and efficiency of the analytical process, which highlights
the need for a more integrated and dynamic collaboration between biologists
and AI systems.

To tackle these challenges, we initiated a substantial partnership with six
biologists and SL researchers, immersing ourselves in their daily research
routines. Through a formative study involving these professionals, we
conducted insightful interviews to discern their needs and expectations. These
discussions not only provided an understanding of their requirements, but also
yielded six design requirements. These requirements are structured into two
tiers: 1) Human-Engaged Knowledge Graph Refinement Based on Metapath111A
sequence of node types that guide a walk from the origin to the destination
node, commonly applied in biomedical network analysis. Strategies and 2)
Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis. We
propose an iterative framework of human-AI collaboration aligned with these
design requirements. Initially, domain experts explore new SL pairs using
predictions and interpretive paths222Interpretive paths are explanatory links
and nodes connecting the primary gene with the predicted gene through
biological relationships. generated by a model trained on the entire data.
During this process, irrelevant or incorrect paths that may introduce noise to
the predictions are eliminated from the KG using appropriate metapath
strategies. Subsequently, the model retrains, allowing domain experts to
iteratively scrutinize predictions and interpretive paths, refining the KG.
This iterative process continuously optimizes predictions and mechanism
exploration, enhancing expert participation and intervention, ultimately
leading to increased trust. Furthermore, mechanism exploration can identify
more persuasive SL pairs for validation through wet lab experiments. To
support this cycle, SLInterpreter is developed to help domain experts organize
and compare interpretive paths across granularities, uncovering potential SL
mechanisms and more persuasive SL pairs. We conducted a case study involving
our collaborative experts, followed by interviews to gather their feedback.
The findings notably underscored the approach’s superiority in delivering
informative domain insights, facilitating decision-making, and presenting an
intuitive interface. In summary, the contributions of this study can be
outlined as follows:

  * •

We collaborated closely with biologists and SL researchers, conducting
insightful interviews to uncover six essential design requirements for
addressing SL mechanisms.

  * •

We introduced the iterative framework SLInterpreter, tailored to meet these
design prerequisites, empowering experts to explore SL pairs and mechanisms,
refine the KG, and foster trust.

  * •

We validated the effectiveness of SLInterpreter through a comprehensive case
study and interviews, demonstrating its superior performance and value.

##  1 Related work

###  1.1 Computational Methods for Synthetic Lethal Prediction

Constrained by the limitations posed by wet lab experiments in screening SL
pairs, numerous computational approaches have been introduced to predict SL
pairs [49, 58]. These approaches fall into three main categories: statistical-
based, network-based, and ML-based methods.

Statistical-based [9, 6, 53, 7] and network-based methods [33, 23] model
existing SL data under specific assumptions. The interpretability of these
methods mainly stems from their ability to reveal statistical or topological
patterns associated with validated SL pairs. For instance, DAISY [18] employed
three statistical methods for different cancer sets to infer SL interactions
from genomic data. Kranthi et al. [22] used a graph-based approach with
centrality measures, assuming that highly connected proteins are crucial and
their knockout leads to lethality. However, these methods may not cover all
data aspects, resulting in high prediction accuracy only for the aspects they
cover, while accuracy may be low for other data [49]. Additionally, this can
introduce biases and challenges in discovering new SL mechanisms. ML-based
methods are designed to identify intricate patterns in large datasets that
defy straightforward analysis, with fewer and more relaxed assumptions about
the data [14], thus improving prediction accuracy for unobserved data. For
instance, Wu et al. [56] developed a k-NN model, suggesting that gene pairs
similar to validated SL pairs have higher SL potential. Lundberg S.M. [35]
enhanced tree-based models’ interpretability by measuring local feature
interactions and using these insights to understand the model’s global
structure. However, most of these classic ML methods still depend on the
manual extraction of gene features based on past knowledge, which can
introduce human biases [49]. While deep learning methods reduce the need for
manual feature engineering, they often lack transparency and function as
opaque black boxes.

Given the limitations of the aforementioned methods, the KG-based GNN model
leverages the extensive validated facts within the KG to learn gene
representations and predict SL relationships with minimal or no explicit
feature engineering [48]. The KG-based GNN model has demonstrated state-of-
the-art predictive accuracy and provides interpretive paths with their weights
through attentive aggregation. Consequently, we built our backend based on the
KR4SL framework [64].

###  1.2 Graph Neural Network Interpretability

Interpreting GNN models is crucial, prompting many efforts to enhance their
interpretability [36, 43, 45, 60, 61]. These efforts are primarily categorized
into three levels of granularity [63]: 1) Instance-Level Explanation: This
aims to elucidate the prediction for each instance by identifying significant
substructures or subgraphs. 2) Model-Level Explanation: This focuses on
comprehending the global decision rules captured by the GNN. 3) Group-Level
Explanation: This provides explanations for a group of predicted instances.

Most GNN interpretability methods focus on Model-Level Explanation [2, 36,
45], aimed at helping data or AI experts understand the structures and
behaviors of models or assist in debugging. Nevertheless, these methods often
overlook the challenges faced by non-AI domain experts. To address this, some
studies have sought to create more intuitive explanations for non-AI experts
using Instance-Level Explanation [43, 62]. For instance, GNNExplainer [60]
identifies critical subgraphs to elucidate GNN model predictions.

While Instance-Level Explanation methods enhance domain experts’ understanding
of a model’s decisions for specific instances, they fall short for those
concerned with potential mechanisms indicated by the model. To address this
limitation, DrugExplorer [50] introduces Group-Level Explanation, which allows
users to categorize drugs based on similar action mechanisms. Inspired by
their approach, our study assists domain users in exploring potential
mechanisms at the Group-Level by analyzing relationships between instances
within interpretive paths throughout iterative interactions with AI. Notably,
our target users are biologists researching SL. Unlike DrugExplorer, which
focuses on drug-to-disease relationships, we emphasize gene-to-gene
relationships. Furthermore, we enable users to iteratively refine AI
predictions and discover new SL mechanisms through cross-granularity
exploration.

Additionally, in GNN research, CorGIE [34] explores graph topology, node
features, and embeddings interplay to decipher GNN operations. Building on
this, GNNLens [20] provides a visual tool assisting ML experts in identifying
potential error patterns, offering insights for optimizing model structures.
However, these tools are unsuitable for non-AI domain experts to explore
predictions and underlying mechanisms. Our work differs by focusing on
assisting domain experts in a deeper exploration of the underlying mechanisms
suggested by GNN models. Through iterative and cross-granularity
visualization, we enable domain experts to rectify knowledge erroneously
acquired by GNN models, facilitating a more profound understanding of
predictions.

###  1.3 Human-AI Collaboration

Human-AI collaboration refers to the interactive cooperation between humans
and AI to jointly address specific tasks, with responsibilities divided based
on the strengths of each party [38, 44]. As AI performance continuously
improves, the ways humans and AI collaborate are constantly evolving, with
three main patterns proposed [44]. The first pattern is AI-assisted decision-
making, where AI provides necessary information and support for humans to make
final decisions. Many studies have seamlessly integrated AI assistance into
the workflow of domain experts [40, 54, 66]. The second pattern, Human-in-the-
loop, involves incorporating expert intervention into the process of training
AI models. This approach aims to train accurate models at minimal cost by
integrating human knowledge and experience [57, 8, 37, 68, 38]. The third
pattern, joint action, involves humans and AI working together towards a
common objective, functioning as a single entity [70, 1, 26, 27, 25, 38].

Building upon existing research, our approach seamlessly integrates the
expertise of biologists with the supportive role of AI. Biologists first
explore new potential SL pairs through the predictions and interpretive paths
generated by the model trained on entire data. Subsequently, we facilitate
biologists’ active involvement in a human-in-the-loop process, assisting them
in refining the knowledge acquired by the model through appropriate metapath
strategies, which results in interpretive paths that progressively reflect
biologists’ expertise and biological principles.

##  2 Formative Study

The formative study aimed to thoroughly understand and evaluate the challenges
faced by SL researchers, their preferences and expectations regarding
collaboration with AI, and their system design requirements. To achieve this,
we conducted semi-structured interviews with four researchers (E1-E4)
specializing in SL mechanisms and two clinical physicians (E5-E6) from a local
university and a hospital (Mean Age = 47.5, SD = 46.6, four males and two
females). Within this group, E1-E2 specialize in screening SL pairs using the
CRISPR/Cas9 technique, focusing on thyroid cancer and glioma, respectively.
E3-E4 are engaged in anticancer drug research, collaborating with E5-E6. Each
participant has extensive research experience in SL and possesses knowledge or
experience in AI-assisted prediction of SL pairs. Through thematic analysis
[5], we extracted valuable insights regarding the challenges faced by domain
experts and summarized the design requirements for our approach. Each
interview session lasted approximately $45$ minutes.

###  2.1 Experts’ Conventional Practices

Our experts emphasized their reliance on wet-lab experiments or statistical
methods in the past for discovering and validating new SL pairs. Specifically,
wet lab experiments require skilled researchers to culture relevant cells.
Moreover, specific gene inhibitors are utilized, necessitating manual
observation and recording of experimental data. For instance, in tumor
experiments, it is crucial to observe and record proliferation-related
experimental data. Some tumors require the assessment of their migration
properties through methods like cell scratching, followed by cell washing or
drug addition observation. If the experiment progresses smoothly, it typically
involves about five researchers and takes approximately half a year.
Considering potential issues such as unexpected cell death or microbial
contamination during cell culture, the duration may extend further. Therefore,
given the considerations of experimental costs, manpower, and equipment needs,
most labs are limited to conducting one to two wet lab experiments at a time.

Given the high cost and limitations of wet lab experiments, our experts
emphasized their need for a method that can predict SL pairs at a lower cost
and with higher accuracy, while also providing reasonable explanations. To
this end, they have attempted to use AI-assisted methods for experimental pair
screening. However, in their attempts, they found that current AI methods lack
sufficient interpretability and tools for mechanism exploration. Moreover,
since the models can only provide one-way prediction results, applying their
expertise to intervene in the AI prediction process proved to be difficult.
The sole form of interaction experts have with the model is to accept or
reject its predictions, which leads to a lack of trust in these models’
predictions.

###  2.2 Experts’ Concerns and Barriers

Domain experts initially voiced concerns over biological data accuracy.
Several interviewees highlighted the importance of reliable and pertinent data
in training AI models. They underscored that “…this basically decides how
accurate the AI predictions are and how sensible the explanations it gives
make sense.” For instance, E1 stated, “Picking the right data to train the
model is really important, and where that data comes from is definitely
something to seriously think about.” E2 mentioned, “A worry we all share is
that if the model is learning, the info we feed it better is true… When we’re
looking into things, if we spot any issues with the data or conclusions, I
hope there’s a way to quickly filter them out.” This reveals the first
challenge: C1. Reliability of biological data in AI training. As experts
analyze the model’s interpretive paths, having effective methods and
interactive tools for screening data and addressing potential errors or
misleading conclusions is vital.

In terms of collaboration with AI, several interviewees expressed concerns
about the current pattern. The prevalent human-AI collaboration approach in SL
involves domain experts passively accepting prediction results. Subsequently,
experts simply choose a few gene pairs for initial experimental validation,
typically those not yet investigated or related to their current research
direction. This collaboration mode entirely overlooks the active involvement
of domain experts in the prediction task, leading to researchers distrusting
the conclusions. As mentioned by E4, “I simply get the results the model spits
out, and when I see some obvious mistakes, there’s no way for me to fix
[them]. It just keeps eroding my trust in AI.” This highlights the second
challenge: C2. Lack of intervention in AI prediction process.

Interviewees also expressed concerns in decision-making, potential SL
mechanism exploration, and continuous collaboration with AI. Specifically,
determining whether to accept and validate AI’s prediction results through
experiments poses a significant challenge. As highlighted by E3, “We’re
talking about over $20,000$ human genes here, and doing experiments for all
those gene pairs? That’s just not economically achievable.” Similarly, E2
pointed out, “…So, AI may predict a bunch of gene pairs, but it takes us at
least half a year to check out a few pairs at the same time. So, we really
hope the predictions are highly accurate and AI can give us a good explanation
of how they came up [with the predictions].” This reveals the third challenge:
C3. High trial-and-error costs. Wet lab experiments are time-consuming and
resource-intensive. Researchers cannot experimentally validate all
predictions, and careful consideration is needed in selecting the next
research targets. For AI predictions, there is a need for more abundant and
reasonable evidence to demonstrate their experimental value.

Furthermore, even if the model provides a plausible explanation for individual
gene pairs, domain experts express greater concern about extracting underlying
mechanisms and identifying commonalities from multiple prediction results. For
example, E2 pointed out, “The prediction results contain too many possible
interpretive paths. Although these paths are sorted by scores, we still need
to focus on at least 10 or so paths, and once too many entities are set up, it
puts a very heavy burden on us to explore possible mechanisms”, highlighting
the challenge: C4. Limitations in SL mechanism exploring. While domain experts
find the commonalities among these explanations intriguing, current methods
remain cumbersome and insufficient in assisting experts to effectively compare
and analyze these commonalities.

While some SL experts acknowledge the accuracy and efficiency of AI
predictions, researchers still harbor widespread concerns regarding sustaining
ongoing collaboration with AI. E5 noted, “We’ve got some AI-related projects
going on, but they’re mostly just starting out… Honestly, AI models are still
kind of new to us compared to the usual methods, so we stick to what we know
best - traditional research methods using stats or genomics.” Similarly, E6
commented, “Yeah, I totally agree that we need [tools] to fix AI when it
learns wrong stuff. In the past, we didn’t have such support, and we’re not
used to working with interactive models like that… So, if those tools come
along, we’ve gotta make sure they actually work by checking how they affect
the model’s performance,” highlighting the final challenge: C5. Lack of
experience in iterative collaboration with AI.

###  2.3 Experts’ Needs and Expectations

After conducting interviews with experts, we’ve compiled a set of requirements
aimed at effectively addressing the discussed challenges. Our approach is
tailored to seamlessly integrate with conventional practices, allowing experts
to grasp prediction model behaviors and delve into the mechanisms behind
successful SL predictions. To delve deeper, we systematically tackle these
requirements in two parts: [KG Refinement]Human-Engaged Knowledge Graph
Refinement Based on Metapath Strategies and [SL Interpretation]Cross-
Granularity SL Interpretation Enhancement and Mechanism Analysis. These parts
operate in an [Iterative Cycle], continuously refining model predictions and
exploring mechanisms, thereby enhancing expert involvement and intervention,
and bolstering trust.

[KG Refinement]Initially, researchers underscored the urgency of conducting a
rapid and comprehensive exploration of training data and interpretive paths,
which is essential for gaining understanding and familiarity with both the
model’s input data and its performance. To tackle challenges concerning the
reliability of biological data [C1], providing a concise summary of training
data and interpretive paths is essential. This forms the basis for our first
design requirement: DR1. Offer a clear summary of training data, prediction
results, and interpretive paths. Furthermore, addressing the urgent need
expressed by several researchers to quickly identify erroneous or noisy data
in the training set through the error-prone interpretive paths indicated by
the model, our second design requirement emerges: DR2. Detect relevant
training data corresponding to erroneous or noisy paths.

[KG Refinement]Addressing the second challenge [C2], which concerns
researchers’ skepticism towards AI predictions due to inadequate intervention,
we introduce DR3. Provide appropriate interventions to filter data
corresponding to interpretive paths. This requirement was consistently
emphasized during semi-structured interviews, highlighting researchers’
worries about the negative impact of insufficient intervention on AI
predictions. To alleviate these concerns, our approach offers a comprehensive
visual analysis of potentially erroneous or noisy data, along with interactive
features allowing users to correct any identified issues promptly.

[SL Interpretation]To address the third challenge [C3], it is proposed that
the system should not only enhance the accuracy of predicting gene pairs
through proper data input but also offer a clear and sufficient representation
of interpretive paths, considering the high cost of trial and error. This
entails the ability to vividly present information about both the genes and
selected entities, along with the attributes of edge relationships. The
emphasis lies on minimizing visual clutter and ensuring the smooth
presentation of both topological and textual information. During semi-
structured interviews, E1 stressed that “only when the interpretive paths are
presented clearly and intuitively, and align well with our domain expertise,
will the predicted results be convincingly justified for wet lab experiments
validation.” This underscores the importance of our fourth design requirement:
DR4. Provide detailed displays of paths and entities for interpretive paths.

[SL Interpretation]Regarding the fourth challenge, identified by researchers
in identifying common potential mechanisms from AI predictions [C4], the
system must clearly present and analyze similarities across various
interpretive paths, which is crucial as it can significantly aid researchers
in comprehending existing mechanisms and uncovering new ones. Acknowledging
the complexity researchers face in exploring common potential mechanisms from
numerous paths, the design must balance cognitive load with information
presentation. According to E1, “Exploring potential mechanisms in SL is highly
complex, and current AI struggles to directly accomplish theoretical
derivations.” Additionally, as noted by E2, “However, identifying
commonalities in AI predictions can suggest fruitful directions, provided
these commonalities are easily extracted and summarized. Comparing them
individually in common AI-assisted systems with limited attention is
impractical and can narrow our focus.” This leads us to our fifth design
requirement: DR5. Comparative analysis and mechanism exploration of different
prediction results and interpretive paths.

[Iterative Cycle]In response to [C5], we introduce the last design
requirement: DR6. Facilitating iterative Human-AI interaction with feedback
and recordings. This requirement is crafted to support domain experts in
overcoming unfamiliarity and uncertainty during their collaboration with AI
systems. By ensuring a seamless exchange of feedback and recording
interactions, we empower domain experts to grasp the repercussions of their
interventions on both datasets and model performance through ongoing
engagement. The essence of this requirement lies in capturing users’ actions
comprehensively, thus enabling them to retrace their steps when faced with
unsatisfactory intervention outcomes or when model metrics fall short of
expectations. This iterative process fosters domain experts’ familiarity with
AI collaboration, allowing them to refine model performance progressively.
Echoing the sentiments of E5, “During the early stages of AI interaction,
keeping up with iterative exploration is key. You know, being able to jot down
all those steps you’re taking? It’s like having a handy tool that lets you go
back and forth, compare stuff, and double-check what you’ve done. This way,
you can make sure your interventions are on point and your analysis is as
sharp as it can be.”

##  3 SLInterpreter

Figure 2: SLInterpreter comprises ptA data processing module, ptB backend
engine, and ptC frontend interface, facilitating an iterative workflow.

Our analysis approach, implemented through SLInterpreter, consists of three
main components: ptA data processing module, ptB backend engine, and ptC
frontend interface (Fig. 2). The data processing module prepares data for the
KG and extracts gene features. The backend engine trains the GNN model with
the KG and gene features, predicting SL pairs and interpretive paths. The
frontend then provides multiple interconnected views for iterative and cross-
granularity SL analysis.

###  3.1 Data Description and Processing

We provide detailed information regarding the data utilized in the backend
engine, including both Gene Feature and Knowledge Graph.

Gene Feature. The gene feature data includes both gene sequence $\mathbb{S}$
and gene description $\mathbb{D}$ sourced from the National Center for
Biotechnology Information (NCBI) database [10], recognized as an authoritative
gene database in the biological domain, ensuring data reliability and
professionalism. Each gene sequence consists of approximately $19,000$ base
pairs (ACGT). However, directly converting these sequences into numerical form
and performing operations like dimensionality reduction would entail
significant computational costs due to the extensive number of genes and the
relatively lengthy gene sequences for each. To address this challenge, we
utilize the k-mer method [11] , a common method of DNA feature extraction in
the biological field, to identify local patterns in gene sequences
$\mathbb{S}$. Meanwhile, we utilize BioBERT [28] to process functional and
descriptive gene information $\mathbb{D}$.

Knowledge Graph. The biological data used to build the Knowledge Graph (KG) is
sourced from two main repositories (Table 1): the SL knowledge graph SynLethDB
[52] and the biomedical knowledge dataset ProteinKG25 [65]. These datasets
include validated gene SL interactions and external knowledge about gene
functions, such as pathways and biological processes. The resulting KG
comprises five types of entities: Gene, Pathway (PW), and three types of Gene
Ontology (GO): Biological Process (BP), Molecular Function (MF), and Cellular
Component (CC). Additionally, it incorporates multiple relationship types,
primarily indicating gene associations with specific paths or annotations by
GO. Entities are connected by these relationships to form triplets, which are
the fundamental components of KG structures, expressing how two entities are
linked (e.g., RPS8—Part of—Cytosol). Collectively, these triplets constitute
the final KG.

Table 1: Data Description. | #Entities | #Relations | #Triples
---|---|---|---
SL Graph | 9746 | 1 | 35374
ProteinKG25 | 42547 | 32 | 361245
Knowledge Graph | 42547 | 33 | 396619

###  3.2 Backend Engine

Predicting SL gene pairs can be seen as akin to link prediction. Following the
methodology described in KR4SL [64], we employ a GNN-based model. This model
predicts potential SL relationships among gene pairs lacking direct
connections by utilizing established relationships to generate prediction
paths and allocate weights to these paths, reflecting the strength of the
predicted SL relationship.

Training Data Construction. According to KR4SL [64], we begin by constructing
a directed heterogeneous KG by integrating the well-established SL graph
SynLethDB [52] with the comprehensive biological dataset ProteinKG25 [65].
Notably, merging these datasets does not increase the number of entities since
all human genes in SynLethDB are already included in ProteinKG25. This
augmented graph serves as the input for the GNN model, enabling it to capture
a rich set of relational patterns and interactions among genes. For
transparency, we have cited the public databases used in this study within our
open-source project333https://github.com/jianghr-shanghaitech/SLInterpreter-
Demo. Additionally, in the context of SLInterpreter, paths that are irrelevant
or less related to SL are iteratively pruned from the KG, refining the input
for the model.

Predicting SL Pairs. Following the encoder-decoder architecture in KR4SL, we
utilize the heterogeneous KG structure to identify potential SL pairs by
tracing relational paths. This approach integrates structural semantics from
the graph with textual semantics extracted from gene descriptions.
Additionally, it employs attentive aggregation among triples with the same
target, enhancing semantic flow through GRU modules at each layer and
accounting for the paths’ weights. In the final encoder layer, candidate SL
partners are pinpointed and evaluated by a score decoder. The decoder ranks
candidates by their potential to form an SL relationship with the target gene,
selecting the top-50 as predicted SL partners, a commonly used metric in such
link prediction tasks [64]. Simultaneously, the model outputs the 3-hop path
containing the predicted link as an interpretive path along with its score.

The triplets in the dataset are divided into 80% training, 10% validation, and
10% test sets. Performance evaluation focused on precision for the top-50
candidates in the test set. The experiments, detailed in Table 1 in the
Appendix, highlighted the model’s efficacy, achieving a precision rate of 53%
for the top-50 candidates. In comparison, using the same data, the average
precision of other similar models [52, 69, 51] was 16.29%, with the second-
best model, NSF4SL [51], achieving 34.7%, which demonstrates the effectiveness
of this model in detecting key gene interactions, providing valuable insights
for cancer research.

###  3.3 Frontend Interface

In collaboration with domain experts, we developed a frontend interface for
exploring biological and genetic data for SL analysis, featuring: 1) Session
View ([KG Refinement]): Includes disease and gene search boxes, a log of
modifier operations, and model performance overview. 2) Embedding View ([SL
Analysis]): Provides an overview of prediction results on feature reduction.
3) Interpretation View ([SL Analysis]): Displays cross-granularity
interpretation paths, and 4) Modifier View ([KG Refinement]): Shows KG
representations for gene and modifier selection in metapath strategies. After
setting the metapath strategies, users can start a new exploration cycle based
on a retrained model ([Iterative Cycle]).

####  3.3.1 Session View

The Session View (Fig. 2\- pt

C

1

& pt

C

5

& pt

C

6

) offers a platform to interact with the model’s prediction outcomes and to
review previous logs and model performance. This view comprises three
subviews: the Panel, the Operation List, and the Model Log.

Panel. Within the Panel (Fig. 2\- pt

C

1

), three search boxes are designed for selecting prediction results at varying
levels. The Disease search box (SLInterpreter: An Exploratory and Iterative
Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction-1)
showcases genes linked to a specific disease, while the Primary Gene search
box (SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System
for GNN-based Synthetic Lethal Prediction-3) enables direct identification of
genes of interest through auto-complete. Upon selecting a Primary Gene,
Partner Gene Table (SLInterpreter: An Exploratory and Iterative Human-AI
Collaborative System for GNN-based Synthetic Lethal Prediction-4) below the
search boxes displays the top-50 Partner Genes from the prediction results in
ascending order, detailing Name, Score, and Rank. Correctly predicted Partner
Genes are highlighted in brown font. We verify the accuracy of predicted
Partner Genes using ground truth data from SynLethDB, sourced from previous
wet lab experiments or relevant literature [52]. Moreover, users can engage
with the table by selecting gene rows, which generates tags in the Partner
Gene search box (SLInterpreter: An Exploratory and Iterative Human-AI
Collaborative System for GNN-based Synthetic Lethal Prediction-5).
Concurrently, the Embedding View and Interpretation View update all prediction
results related to the selected Partner Gene. Operation List. The Operation
List (Fig. 2\- pt

C

5

) records user interactions with the KG in chronological order, featuring
collapsible text boxes beneath each action for users to elaborate on their
operations and corresponding notes (DR6). Users can select metapaths using
checkboxes. Undesired metapaths can be removed by clicking . Once the
selection of metapaths is finalized and  is clicked, The backend automatically
deletes the corresponding paths, retrains the model, and displays its
performance in the Model Log. Model Log. The Model Log (Fig. 2\- pt

C

6

) displays grouped bars detailing model performance indicators, including
Recall, NDCG, and Precision. Indicators are color-coded with bar length
representing values. The vertical arrangement allows intuitive performance
comparison across models. Users can access models by clicking the
corresponding bars.

####  3.3.2 Embedding View

The Embedding View (Fig. 2\- pt

C

2

) offers users an overview of clustering based on gene features, along with
model prediction results and validated SL partners (DR1), enabling exploration
of SL pairing patterns within comparable gene groups (DR5). To fully leverage
gene attributes, including sequences $\mathbb{S}$ and descriptions
$\mathbb{D}$, we use concatenation fusion methods to merge the processed data.
We then apply UMAP [39] to generate the embedding result due to its efficiency
in compressing high-dimensional data into a lower-dimensional representation.
Our experiments (Appendix Fig. 1&2&3) showed that UMAP outperformed PCA [55]
and t-SNE [47] in preserving global data structure and execution speed. PCA
often overlooks crucial biological variations [19], while t-SNE primarily
reveals local structures without effectively showing similarities between
clusters [31]. Both PCA and t-SNE also perform slowly with large datasets.
Although UMAP is non-linear, meaning distances in the Embedding View are not
linear, it effectively preserves local distances over global distances,
enhancing clustering within local regions, which makes it suitable for our
focus on the correspondence between local clusters. Consequently, the dashed
lines connecting nodes illustrate pairing relationships between genes rather
than distance comparisons, helping users identify patterns where genes from
one cluster frequently link to genes from another cluster.

Visual Design. The Embedding View enhances disease exploration through
intuitive visual cues. Upon selecting a disease from the Disease search box,
the Embedding View highlights associated genes with distinct $\bullet$ points.
The surrounding Radars directly project the relative locations of Partner
Genes onto concentric circles centered on the Primary Gene. The top-50
predicted Partner Genes are highlighted in $\bullet$ and correctly predicted
Partner Genes are shown in $\bullet$ (Fig. 3\- A). Users can employ semantic
zooming to manage overlapping points or observe radar patterns more clearly.
Hovering over a highlighted point reveals an enlarged Radar (Fig. 3\- B),
facilitating the identification of tightly clustered Partner Genes with common
pairing patterns.

Figure 3: Glyph Designs for Embedding View. A is the overview of Radar in
disease searching. B is the enlarged Radar. C is the Rank Indicator.

Once a Primary Gene is identified, users can select it in the Primary Gene
search box for further investigation. Upon selecting, the corresponding
Primary Gene remains highlighted, while other genes revert to the default
color. Simultaneously, the Embedding View highlights top-50 predicted Partner
Genes $\bullet$ with dashed lines connecting to the Primary Gene, validated
Partner Genes of the Primary Gene $\bullet$, and correctly predicted Partner
Genes $\bullet$. Each top-50 predicted Partner Gene is encircled by Rank
Indicator (Fig. 3\- C), an arc indicating its rank of the Partner Gene. The
arc starts at the top and lengthens clockwise as the rank increases, with a
bar representing the end of the arc, helping to discover clusters of similar
ranked genes at a glance. When tags are created in the Partner search box,
corresponding points are highlighted $\bullet$, and the dashed line turns
solid.

Interaction. When no Disease or Primary Gene is selected, users can lasso all
genes to explore patterns among genes with similar features, with the selected
genes highlighted $\bullet$. Once a Disease is chosen, lassoing among disease-
related genes helps explore patterns within genes linked to the same disease.
Similarly, when a Primary Gene is selected, lassoing among the top-50 Partner
Genes enables the exploration among Partner Genes belonging to the same
Primary Gene.

####  3.3.3 Interpretation View

The Interpretation View (Fig. 2\- pt

C

3

) offers users an across-granularity analysis of paths: spanning from
comprehensive cross-gene analyses (DR5) to intricate single-path analyses
(DR4). To enhance the clarity of metapath content and flow, we break them down
based on path hierarchy into three primary elements: 1) the name, type, and
proportion of the current hierarchical node; 2) the flow from the current
hierarchical node type to the category of the subsequent hierarchical node,
and 3) the names of the paths linking to the next hierarchical node.

Visual Design. To effectively illustrate the name, type, and proportion of
entities on each layer while mitigating excessive overlapping of connecting
lines, we employ Voronoi Treemaps [3]. This method uses the weight of
different entities within interpretive paths as their value, dividing space
into irregular polygons, optimizing display space utilization, and offering
clear differentiation between various entities. Clicking on an entity’s
polygon will highlight other entities connected to it. Consequently, entities
belonging to the same category, such as genes or biological processes (BP),
are assigned the same color and grouped together within the same area. There
are a total of five categories of entities: Gene, Cellular Component (CC),
Biological Process (BP), Molecular Function (MF), and Pathway, as well as 33
types of path relationships, which are represented using consistent colors.

The Entity Flow Bar (Fig. 4\- 7) and the Path Bar (Fig. 4\- 4) are both
presented as horizontally arranged stacked bars. The Entity Flow Bar is
positioned beneath the Entity Treemap (Fig. 4\- 6) and corresponds to the
categories of entities depicted in the Entity Treemap above. It illustrates
the proportion of the flow from a specific category of entity to the
succeeding layer’s entity category. The Entity Flow Bars for distinct entity
categories operate independently, yet entities of the same category are color-
coded identically to streamline user comprehension. Flow lines traverse
through the designated areas defined by the Entity Flow Bar, effectively
distinguishing the connections. On the other hand, the Path Bar is a
comprehensive stacked bar including every type of relation observed, along
with its relative proportion. By analyzing these proportions, users can
discern the frequency of each relation type. Moreover, connections stemming
from the Entity Flow Bar are linked to the Path Bars representing different
edges based on their corresponding relation type, which illustrates the
connection between the source entities and the relations.

Design Alternatives. In the iterative design process, we explored two
alternatives. The first alternative (Fig. 4\- A) includes the Sankey Flow
(Fig. 4\- 1) illustrating entity flow, and the Sankey Bar (Fig. 4\- 2) showing
entity proportion within the interpretation path. In the Packchart diagram,
Entity Nodes (Fig. 4\- 3) display entity type and proportion, while the Path
bar (Fig. 4\- 4) shows path type and proportion with connecting lines. This
design maintains consistent colors for entity type and accurately represents
entity percentage and weight, but confuses hierarchical correspondence between
the Sankey and Packchart diagrams, requiring users to toggle between the two.
Moreover, the use of nodes in the Packchart is spatially inefficient. In the
second alternative (Fig. 4\- B), we integrate both diagrams by merging Entity
Nodes and Sankey Bar into the Entity Matrix (Fig. 4\- 5), where each matrix’s
size indicates the entity’s weight. Retaining Sankey Flow and Path Bar, this
design results in considerable overlap in entities’ vertical coordinates,
leading to disorganized connections and compromised visibility. Furthermore,
the presentation of the hierarchy remains confusing. In the final design (Fig.
4\- C), Entity Treemap (Fig. 4\- 6) eliminates coordinate overlap with
irregular shapes, and Entity Flow Bar (Fig. 4\- 7) represents the proportion
of different entity types, replacing Sankey Flow. This design displays
hierarchical paths without overlap, ensuring clear and uninterrupted layer
connections within an integrated view.

Figure 4: Design alternatives for Interpretation View. A is an alternative
based on Sankey and Packchart. B is an alternative based on Sankey and node
matrix. C is the final design based on Voronoi Treemap.

####  3.3.4 Modifier View

The Modifier View (Fig. 2\- pt

C

4

) incorporates both the Knowledge Graph (SLInterpreter: An Exploratory and
Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal
Prediction-8) and Metapath Modifier (Fig. 5) functionalities to aid users in
exploring the KG (DR1), pinpointing pertinent training data (DR2), and
devising human-engaged strategic refinement of metapaths to filter out
irrelevant or noisy data within the dataset(DR3).

Figure 5: Metapath Modifier design for Modifier View. A Design for the Entity-
level Summary. B Design for the Metapath Strategy. C Design for the Path-level
Summary. D Granularity Level Flow for the Path-level Summary, H represents
High Granularity and L represents Low Granularity.

Visual Design of the KG. Upon selecting a Primary Gene in the Panel, the KG
displays entities originating from this gene. Inspired by the design of
EgoComp [32], we used an ego-network layout to show the KG structure. Entities
of the same layer are placed on circular arcs with equal radius,
preferentially grouping similar entities. The colors of entities in the KG
correspond to those in the Interpretation View, ensuring design consistency.
To manage the exponential increase in entities, we display up to 2-hop
connections to balance path length and entity count. When users hover over
different entities, the paths leading to that entity and all related entities
along the path are highlighted, with their names displayed. This interactive
feature allows users to leverage their expertise to evaluate if entities
across various paths are biologically related to SL or have plausible links to
the Primary Gene. Automating selection based on interactions with the
Interpretation View was also considered, but expert feedback showed it could
lead users to select many irrelevant paths or paths not present in the KG.
Therefore, manual selection was chosen for simplicity and to minimize errors.

Concept of the Granularity Level Flow. To assist users in formulating detailed
metapath strategies and deriving insights, we introduced the Granularity Level
Flow (Fig. 5\- D). This flow illustrates the transition of metapath
granularity from high (H) to low (L), moving left to right, and displays the
hierarchy of metapath strategies across granularity levels. In this flow,H-H-
Hrepresents the metapath with the highest granularity, 1-Low Granularity
represents the metapath with one low granularity entity, and 2-Low Granularity
represents the metapath with two low granularity entity.L-L-Ldenotes specific
triplets, with a count of 1. Users begin formulating their metapath strategies
by selecting a specific entity from the KG, with the default granularity
beingL-L-L. By converting a low-granularity entity to high-granularity, more
paths are included in the formulated metapath strategies, resulting in the
removal of more paths from the KG due to the expanded selection range.
Therefore, user selections progress from right to left, gradually increasing
in granularity. This linear progression helps users determine the appropriate
level of granularity, minimizing the risk of accidental or erroneous deletions
caused by sudden changes in granularity.

Visual Design of the Metapath Modifier. When a specific entity is selected,
the Metapath Modifier generates a horizontal metapath display box on the
right, linked with the entity. This display box, shaped by usage habits from
expert interviews, is intended to show information in a table format.
Initially, the display box showcases the selected path (Fig. 5\- pt

B

1

), with each entity’s higher granularity elements (Fig. 5\- pt

B

2

) displayed beneath it. Higher granularity elements denote wider biological
categories, each potentially encompassing several entities of lower
granularity. For example, beneath the entity A1BG, the metapath element Gene
is displayed. Users can modify metapath strategies by clicking corresponding
entity granularities (Fig. 5\- pt

B

1

& pt

B

2

). A frequency histogram (Fig. 5\- A) is presented above the source (Fig. 5\-
1) and target entities (Fig. 5\- 2), indicating the distribution of path
counts that begin or end with each type of entity. Given the wide range and
long-tail distribution of entity frequencies, we applied a log transformation
to the x-axis to enhance the comparison of the data, resulting in varying bar
widths. Below the histogram, a horizontal box plot (Fig. 5\- 3) shows the
central tendency and dispersion of frequency data. The position of the current
entity is marked red, helping users grasp its relative placement within the
dataset. To provide detailed information, hovering over a bar in the histogram
reveals its interval and corresponding value. To further illustrate the
relative proportions of metapath strategies with varying granularity, we have
designed two vertical bars (i.e., Primary Bar and Secondary Bar) (Fig. 5\- 4 &
5) situated in the Path-Level Summary (Fig. 5\- C). These bars are designed in
a manner akin to the Granularity Level Flow (Fig. 5\- D).

Primary Bar. The left bar (Fig. 5\- 4), designated as the Primary Bar, of
which the height corresponds to the number of metapaths with the highest
granularity, such as Gene—All_edge—Pathway (H-H-H). Since each entity offers
two granularities to choose from—low (Fig. 5\- pt

B

1

) or high (Fig. 5\- pt

B

5

), the Primary Bar reflects this distinction. Within the Primary Bar, three
sub-segments (Fig. 5\- 6) depict the relative proportions of metapath
strategies with exactly one low granularity entity (i.e.,L-H-H,H-L-HandH-H-L)
selected within the metapath strategies of highest granularity (i.e.,H-H-H),
with colors indicating the type of low granularity entity.

Secondary Bar. The height of the right bar (Fig. 5\- 5), termed the Secondary
Bar, corresponds to the number of metapaths involving one low granularity
selected entity (i.e.,L-H-H,H-L-HorH-H-L). Its sub-segment heights depict the
relative proportions of metapath strategies involving two selected low
granularity entities within the metapaths of one selected low granularity
entity. There are two cases regarding the display of the Secondary Bar: 1) If
the selected metapath strategy includes exactly one entity of low granularity,
the Secondary Bar is displayed as a complete bar. Its height represents the
number of paths included in the selected metapath strategy, while the sub-
segments’ height represents the proportion of the two sub-strategies of the
selected metapath strategy within it. 2) If the selected metapath strategy
includes exactly two entities of low granularity, the 2-Low Granularity
metapath strategies have two parents strategies, such asL-L-HamongH-L-HandL-
H-H. In this case, the Secondary Bar is divided into the top (Fig. 5\- 9) and
bottom (Fig. 5\- 10) bars to represent the two proportions, (e.g., L-L-H/H-L-
HandL-L-H/L-H-H).

Connecting Rules of Primary and Secondary Bars for Granularity Selection.
Based on the number of low granularity entities selected in the strategy, the
Primary Bar and the Secondary Bar utilize two connecting rules to represent
corresponding information: 1) In cases where exactly one entity of lower
granularity is selected, such as Gene—All_edge—IL6-mediated_si… (H-H-L) (Fig.
5\- pt

C

1

), the target entity (Fig. 5\- 2) links to the corresponding sub-segment (Fig.
5\- 6) on the Primary Bar (Fig. 5\- 4). This linkage indicates the strategy’s
proportion within the highest granularity strategy (H-H-H). Two lines (Fig.
5\- 7) extend from the Primary Bar to the Secondary Bar, delineating the
relative proportions of two lower granularity strategies based on the selected
entity within the current metapath strategy (H-H-L). Since these two lower
granularity strategies are not truly selected, they are linked in dash lines.
In this example, the L represents the shared low granularity entity, making
the 2-Low Granularity strategies on the Secondary Bar subsets of the current
strategy. 2) In cases where two entities of lower granularity are selected,
such as A1BG—enables—MF (L-L-H) depicted in Fig. 5\- pt

C

2

, the strategies at this granularity are subsets of two higher-level
strategies (H-L-HandL-H-H), each involving 1-Low Granularity entity.
Consequently, the proportions of these two parent strategies within the
highest granularity are simultaneously displayed in the Primary Bar and
connected to the target entity (Fig. 5\- 8). In this scenario, the Secondary
Bar (Fig. 5\- 9 & 10) shows the proportion of the current metapath selection
strategy (L-L-H) within two higher granularity strategies (H-L-H&L-H-H).

Design Takeaway. Through observation and interaction with the metapath display
box, users can thoroughly assess the proportions and metapaths with specific
granularity and derive insights from them. For instance,
A1BG—PARTICIPATES_GpPW—IL6-MEDIATED_… (Fig. 5\- pt

B

1

), where its H-H-L (Fig. 5\- pt

C

1

) subset occupies a smaller proportion withinH-H-H, and the H-L-L subset
occupies a larger proportion withinH-H-L, user can discern that despite the
fewerH-H-Lpaths, the majority of paths between Gene (H) and IL6-MEDIATED_… (L)
are connected by relation PARTICIPATES_GpPW (L). However, some paths are
connected by other relations, like involved_in (L). Gaining this insight, if
user determines that Gene should not be connected to IL6-MEDIATED_… through
PARTICIPATES_GpPW, he can opt for the H-L-Lmetapath instead ofH-H-L, thereby
ensuring the exclusion of any undesired paths from deletion.

##  4 Evaluation

To evaluate the SLInterpreter’s effectiveness, we conducted a case study with
E1-E3, who had participated in our formative study. Following the Co-discovery
Learning Protocol [30], one author briefly guided the system’s operation while
E1 operated the system and E2 and E3 engaged in discussions. We then conducted
expert interviews to gather insights and feedback on their user experience.
The entire process lasted about 2 hours: 30 minutes for familiarizing
participants with the system, followed by two 50-minute rounds of iterative
exploration.

###  4.1 Case Study

Investigating CDK1 and Identifying Problem. E1, a thyroid cancer specialist,
initiated by selecting thyroid cancer in the Disease search box
(SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for
GNN-based Synthetic Lethal Prediction-1). Within the Embedding View, he
identified a region (SLInterpreter: An Exploratory and Iterative Human-AI
Collaborative System for GNN-based Synthetic Lethal Prediction-2) where
accurately predicted Partner Genes are closely clustered. Employing semantic
zoom, E1 noticed a cluster of KRAS predictions in the upper right area. Given
the extensive study of KRAS in SL and its role in the RAS/MAPK signaling
pathway regulating cells, the experts held a solid understanding of KRAS.
Observing that correctly predicted Partner Genes of CDK1 also cluster in the
upper right, similar to KRAS, E1 selected CDK1 in the Primary Gene search box
(SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for
GNN-based Synthetic Lethal Prediction-3). However, he found the interpretive
paths for CDK1 predominantly linked by gene, making these paths less
convincing. Therefore, E1 shifted focus to different path types. By clicking
on the non-gene entities (Fig. 6\- 1), E1 discovered that these paths
primarily lead to MYC and RPL13. Among the non-gene entities,
sensory_perception_of_smell has the highest weight in BP, which E2 deemed
irrelevant to SL.

Figure 6:  1 E1 clicked on the non-gene entities during investigating CDK1.

Analysing the Interpretive Path of CDK1$\rightarrow$MYC. Delving deeper into
the embedding of CDK1, E1 noted three correctly predicted genes: KRAS, ranked
$13^{th}$, MYC at $27^{th}$, and RPL13 at $33^{rd}$. After excluding higher-
ranked results that were predicted solely based on SL relationships and had
lower biological significance, E1 found the remaining ranks sufficiently high
to merit further exploration. Notably, CDK1 and KRAS are close in the
embedding, as are MYC and RPL13. This proximity suggests a similarity in their
features. Given their relevance to thyroid cancer, the clustering of CDK1 and
KRAS is intriguing to E1. He emphasized that targeted therapy for RAS gene
mutations is a key focus in cancer treatment, with extensive research ongoing.
Consequently, E1 was interested in how the model explains the predictions for
the other two genes. Drawing from his previous observations, E1 proceeded by
selecting the higher-ranked MYC in the Partner Gene Table (SLInterpreter: An
Exploratory and Iterative Human-AI Collaborative System for GNN-based
Synthetic Lethal Prediction-4), and the corresponding tag is generated
(SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for
GNN-based Synthetic Lethal Prediction-5). MYC is a critical oncogene encoding
the transcription factor MYC protein, pivotal in cell growth, division, and
metabolism processes. Analyzing the CDK1$\rightarrow$MYC interpretive path, E1
observed that the Entity Treemap’s middle layers primarily consist of Genes,
BP, and CC. Contrary to common patterns observed in CDK1’s pairing with
Partner Genes, the Entity Flow Bar (SLInterpreter: An Exploratory and
Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal
Prediction-6) indicates that gene-gene connections are not predominant here.
Additionally, the Path Bar (SLInterpreter: An Exploratory and Iterative Human-
AI Collaborative System for GNN-based Synthetic Lethal Prediction-7) displays
a smaller ratio of SL_GsG, which indicates validated SL partner relationship,
compared to those belonging to BP and CC. The linkage between CDK1 and MYC
mainly involves Gene$\rightarrow$BP paths, such as
CDK1—involved_in—DNA_replication—has_part—DNA_biosynthetic_process—involved_in_inv—MYC.
E1 found this path consistent with his domain knowledge, particularly as MYC
overexpression, a transcription factor driving cell growth, may enhance the
reliance on CDK1 for DNA biosynthesis.

Refining the KG. From the initial exploration, E2 identified an implausible
path
CDK1—SL_GsG—FARSA—involved_in—sensory_perception_of_smell—involved_in_inv—MYC
and similar paths inconsistent with his expertise. To investigate potential
data-level issues causing these irrational predictions, he employed the
Modifier View. E1 first narrowed the investigation scope to
CDK1$\rightarrow$Gene$\rightarrow$BP in the Knowledge Graph. Since CDK1
belongs to the gene category, he began exploring from the dashed green areas
(SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for
GNN-based Synthetic Lethal Prediction-8). He noticed that the path of
Gene$\rightarrow$sensory_perception_of_smell indeed appears in the Knowledge
Graph, despite its irrelevance to SL. Selecting this path in the Knowledge
Graph (SLInterpreter: An Exploratory and Iterative Human-AI Collaborative
System for GNN-based Synthetic Lethal Prediction-9), E1 then refined metapath
strategies in the Metapath Modifier. He first noticed the histogram of
sensory_perception_of_smell (SLInterpreter: An Exploratory and Iterative
Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction-10),
which indicates that among all entities in its high granularity category of
Biological Processes (BP), this entity frequently appears as the endpoint of
such metapaths (Gene—All_edge—BP). Based on his expertise, E1 determined that
any path involving sensory_perception_of_smell is irrelevant to SL. Therefore,
E1 selected the L-H-L strategy by clicking on All edge (SLInterpreter: An
Exploratory and Iterative Human-AI Collaborative System for GNN-based
Synthetic Lethal Prediction-11).

E3 noted the relatively low height of the primary bar, indicating that the
proportional relationship of FARSA—All edge—sensory_perception_of_smell
(L-H-L) within its parent set, Gene—All edge—sensory_perception_of_smell
(H-H-L) (SLInterpreter: An Exploratory and Iterative Human-AI Collaborative
System for GNN-based Synthetic Lethal Prediction-12), was not statistically
significant. This indicates that many other Genes are also connected to
sensory_perception_of_smell, which are all irrelevant to SL. Consequently, E1
selected the H-H-L metapath strategy by clicking on Gene (SLInterpreter: An
Exploratory and Iterative Human-AI Collaborative System for GNN-based
Synthetic Lethal Prediction-13). Upon doing so, E1 observed the proportional
relationships of the L-H-L and H-L-L sub-strategies to H-H-L (SLInterpreter:
An Exploratory and Iterative Human-AI Collaborative System for GNN-based
Synthetic Lethal Prediction-14). Specifically, there is only one occurrence of
L-H-L, while H-L-L dominates the entire bar. This suggests that almost all
Genes are associated with sensory_perception_of_smell through the involved_in
relationship. Simultaneously, FARSA represents only a fraction of Gene
connected to sensory_perception_of_smell, even when considering all possible
edge types. These findings aligned with the experts’ expectations.
Consequently, E1 added this strategy to the Operation List and entered
“sensory_perception_of_smell is irrelevant to synthetic lethality” in the text
box (SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System
for GNN-based Synthetic Lethal Prediction-15). Subsequently, E1 clicked
(SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for
GNN-based Synthetic Lethal Prediction-16) to retrain the model. Upon receiving
the retrained model and its performance metrics from the backend, E1 observed
from the Model Log that the new model’s performance was slightly improved
compared to the original model. The accuracy increased by $0.88\%$, an
$1.66\%$ improvement over the original rate, while the deleted path data
accounted for only $0.13\%$ of the total data.

Figure 7:  1 E1 highlighted EGFR’s interpretive paths to reveal patterns
similar to MYC and RPL13.

Exploring New Potential SL Partner of CDK1. E1 proceeded by selecting the
retrained model(SLInterpreter: An Exploratory and Iterative Human-AI
Collaborative System for GNN-based Synthetic Lethal Prediction-17) to initiate
an iterative exploration. This time, he directly targeted CDK1 in the Primary
Gene search box. Upon examining the Interpretion View of CDK1, E2 noticed that
the newly generated paths no longer include sensory_perception_of_smell.
Furthermore, MYC and RPL13 remained accurately identified, maintaining their
rankings at $23^{rd}$ and $29^{th}$. In the Embedding View, E1 noted that
RPL13 and EGFR are close to MYC. Additionally, through the Rank Indicator
(SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for
GNN-based Synthetic Lethal Prediction-18), E3 noticed that these three genes
share similar rankings. Notably, EGFR, ranked $31^{st}$, indicates a potential
but unidentified SL relationship with CDK1. To investigate potential patterns
among them, E1 conducted a lasso operation (SLInterpreter: An Exploratory and
Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal
Prediction-19). Among these genes’ interpretive paths, E3 observed a high
proportion for the pattern
Gene$\rightarrow$BP$\rightarrow$BP$\rightarrow$Gene. By highlighting EGFR’s
interpretive paths (Fig. 7\- 1), E3 observed striking similarities to the
patterns of the other two correctly predicted genes. At this point, E3
remarked, “If the model could give us clear paths like this for every
prediction, and we could compare them with other known patterns, then I’d feel
more confident in trusting those predictions and putting resources into
checking them.

###  4.2 Expert Interview

System Design and Feedback. Biologists confirmed the effectiveness of
SLInterpreter in a human-AI collaboration that enhances model interpretability
and explores SL mechanisms. E1 remarked, “The design of the Session View fits
well with my habits of using biological databases; we can start exploring from
a specific disease of interest.” E2 praised the Embedding View for quickly
indicating potential common patterns and appreciated the Rank Indicator, which
helps determine the ranks of predicted genes. E3 was pleased with the system’s
comprehensive and intuitive features that aid in removing irrelevant or
erroneous data, especially given the high number of data proven to be
erroneous or biased. He suggested the system could further improve by
automatically collecting and displaying suspicious data. Additionally, E1 and
E3 noted that traditional AI-assisted methods required sequential reviewing of
a large number of materials based on confidence ranking alone, typically
taking two to three days to filter $50$ predicted results. This lengthy
duration was due to the extensive time spent retrieving potential associations
between predicted SL pairs, as the information was scattered and lacked
targeted retrieval, often resulting in unexpected delays. The discontinuity of
this review process also reduced their confidence in the results. In contrast,
using SLInterpreter made their exploration more targeted, enabling them to
review $50$ predicted results within five to eight hours, considering the
varying time required for retraining the model. SLInterpreter eliminated the
need for further retrieval of obviously erroneous prediction paths, and the
continuity and intervenability of the process increased their confidence in
the results.

Learning Curve. The experts noted that the system’s design matches their usage
habits and the interaction is intuitive. However, there is a certain learning
curve associated with the Metapath Modifier in the Modifier View and the whole
iterative cycle. The entire system requires some time to understand and master
(about 20 minutes), and learning through specific cases can make the learning
curve more gradual. Nevertheless, the experts still believe that SLInterpreter
can help them gain more insights from their explorations and have greater
confidence in the potential SL pairs they discover.

##  5 Discussion and Limitation

Lessons Learned. Our collaboration with biologists has provided valuable
insights. First, SL-related biological data is often extensive and high-
dimensional, making rule-based filtering insufficient and necessitating
biologists’ expertise. However, exhaustive evaluations by biologists are
unsustainable, highlighting the importance of leveraging AI or visualization
tools to focus their assessments. Second, we recognized the need to refine
metapath granularity levels. While biological knowledge categorization is
intricate, adding granularity levels for certain entity types can rapidly
expand metapath strategies. Thus, balancing granularity levels with user
burden is crucial for effeteness.

Generalization and Scalability. The SLInterpreter enhances interpretability
and facilitates cross-granularity exploration for AI models through
interpretive paths. Validated by biologists, it is applicable to various
network predictive tasks and models across multiple domains, including drug
prediction, social network forecasting, and recommendation systems. To ensure
scalability, features like semantic zooming and highlighting have been
implemented to prevent visual clutter. Additionally, for larger entity sets in
knowledge graphs, stacking or grouping nodes can further enhance spatial
information capacity.

Limitations. This study presents several limitations. First, the dataset has
limited biological relationships. Despite SL spans various domains like
biomolecular studies and genomics, resulting explanations remain somewhat
broad and constrained. Second, the model primarily predicts new SL pairs via
existing pairs, which may have limited value due to relatively few connected
paths via biological information. Third, experts note that SL gene pairs are
not exclusively limited to pairs [42]. Although rare, confirmed instances of
SL relationships involving multiple genes exist, expanding prediction from two
genes to more, complicating their connections. This highlights the need for
comprehensive systems and visualizations for complex SL networks.
Additionally, due to extended cycles of wet lab experiments, adequate
quantitative experiments were not feasible within the available time.

##  6 Conclusion and Future Work

This study introduces an iterative Human-AI collaboration framework using
SLInterpreter, aimed at 1) Human-Engaged Knowledge Graph Refinement based on
Metapath Strategies and 2) Cross-Granularity SL Interpretation Enhancement and
Mechanism Analysis for domain experts. A case study and expert interviews
demonstrate SLInterpreter’s ability to efficiently discover new SL pairs,
provide substantial interpretability, and offer effective methods for
commonality exploration. Future plans include extending the analysis from
single paths to complex networks involving multiple genes, further integrating
additional data sources, and expanding the system to other GNN prediction
tasks.

###### Acknowledgements.

We would like to express our gratitude to our domain experts and the anonymous
reviewers for their insightful comments. This work is funded by grants from
the National Natural Science Foundation of China (No. 62372298), the Shanghai
Frontiers Science Center of Human-centered Artificial Intelligence (ShangHAI),
and the Key Laboratory of Intelligent Perception and Human-Machine
Collaboration (ShanghaiTech University), Ministry of Education.

## References

  * [1] Z. Ashktorab, Q. V. Liao, C. Dugan, J. Johnson, Q. Pan, W. Zhang, S. Kumaravel, and M. Campbell.  Human-ai collaboration in a cooperative game setting: Measuring social perception and outcomes.  Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2):1–20, 2020. doi: 10 . 1145/3415167
  * [2] F. Baldassarre and H. Azizpour.  Explainability techniques for graph convolutional networks.  arXiv preprint arXiv:1905.13686, 2019. doi: 10 . 48550/arXiv . 1905 . 1368
  * [3] M. Balzer and O. Deussen.  Voronoi treemaps.  In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pp. 49–56, 2005. doi: 10 . 1109/INFVIS . 2005 . 1532128
  * [4] R. Cai, X. Chen, Y. Fang, M. Wu, and Y. Hao.  Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers.  Bioinformatics, 36(16):4458–4465, 2020. doi: 10 . 1093/bioinformatics/btaa211
  * [5] V. Clarke and V. Braun.  Thematic analysis.  The journal of positive psychology, 12(3):297–298, 2017. doi: 10 . 1080/17439760 . 2016 . 1262613
  * [6] N. Conde-Pueyo, A. Munteanu, R. V. Solé, and C. Rodríguez-Caso.  Human synthetic lethal inference as potential anti-cancer target gene detection.  BMC Systems Biology, 3(1):1–15, 2009. doi: 10 . 1186/1752-0509-3-116
  * [7] X. Deng, S. Das, K. Valdez, K. Camphausen, and U. Shankavaram.  Sl-biodp: multi-cancer interactive tool for prediction of synthetic lethality and response to cancer treatment.  Cancers, 11(11):1682, 2019. doi: 10 . 3390/cancers11111682
  * [8] J. J. Dudley and P. O. Kristensson.  A review of user interface design for interactive machine learning.  ACM Transactions on Interactive Intelligent Systems (TiiS), 8(2):1–37, 2018. doi: 10 . 1145/3185517
  * [9] X. Feng, N. Arang, D. C. Rigiracciolo, J. S. Lee, H. Yeerna, Z. Wang, S. Lubrano, A. Kishore, J. A. Pachter, G. M. König, et al.  A platform of synthetic lethal gene interaction networks reveals that the gnaq uveal melanoma oncogene controls the hippo pathway through fak.  Cancer cell, 35(3):457–472, 2019. doi: 10 . 1016/j . ccell . 2019 . 01 . 009
  * [10] L. Y. Geer, A. Marchler-Bauer, R. C. Geer, L. Han, J. He, S. He, C. Liu, W. Shi, and S. H. Bryant.  The ncbi biosystems database.  Nucleic acids research, 38(suppl_1):D492–D496, 2010. doi: 10 . 1093/nar/gkp858
  * [11] M. Ghandi, D. Lee, M. Mohammad-Noori, and M. A. Beer.  Enhanced regulatory sequence prediction using gapped k-mer features.  PLOS Computational Biology, 10(7):1–15, 07 2014. doi: 10 . 1371/journal . pcbi . 1003711
  * [12] C. Gourley, J. Balmaña, J. A. Ledermann, V. Serra, R. Dent, S. Loibl, E. Pujade-Lauraine, and S. J. Boulton.  Moving from poly (adp-ribose) polymerase inhibition to targeting dna repair and dna damage response in cancer therapy.  Journal of Clinical Oncology, 37(25):2257–2269, 2019. doi: 10 . 1200/JCO . 18 . 02050
  * [13] J. Huang, A. Mishra, B. C. Kwon, and C. Bryan.  Conceptexplainer: Interactive explanation for deep neural networks from a concept perspective.  IEEE Transactions on Visualization and Computer Graphics, 29(1):831–841, 2023. doi: 10 . 1109/TVCG . 2022 . 3209384
  * [14] H. Ij.  Statistics versus machine learning.  Nat Methods, 15(4):233, 2018. doi: 10 . 1038/nmeth . 4642
  * [15] A. Jacunski, S. J. Dixon, and N. P. Tatonetti.  Connectivity homology enables inter-species network models of synthetic lethality.  PLoS computational biology, 11(10):e1004506, 2015. doi: 10 . 1371/journal . pcbi . 1004506
  * [16] J. Jakubik, J. Schöffer, V. Hoge, M. Vössing, and N. Kühl.  An empirical evaluation of predicted outcomes as explanations in human-ai decision-making.  In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 353–368. Springer, 2022. doi: 10 . 48550/arXiv . 2208 . 04181
  * [17] H. Jariyal, F. Weinberg, A. Achreja, D. Nagarath, and A. Srivastava.  Synthetic lethality: a step forward for personalized medicine in cancer.  Drug Discovery Today, 25(2):305–320, 2020. doi: 10 . 1016/j . drudis . 2019 . 11 . 014
  * [18] L. Jerby-Arnon, N. Pfetzer, Y. Y. Waldman, L. McGarry, D. James, E. Shanks, B. Seashore-Ludlow, A. Weinstock, T. Geiger, P. A. Clemons, et al.  Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.  Cell, 158(5):1199–1209, 2014. doi: 10 . 1016/j . cell . 2014 . 07 . 027
  * [19] D. Jiang, C. Tang, and A. Zhang.  Cluster analysis for gene expression data: a survey.  IEEE Transactions on Knowledge and Data Engineering, 16(11):1370–1386, 2004. doi: 10 . 1109/TKDE . 2004 . 68
  * [20] Z. Jin, Y. Wang, Q. Wang, Y. Ming, T. Ma, and H. Qu.  Gnnlens: A visual analytics approach for prediction error diagnosis of graph neural networks.  IEEE Transactions on Visualization and Computer Graphics, 2022. doi: 10 . 1109/TVCG . 2022 . 3148107
  * [21] W. G. Kaelin Jr.  The concept of synthetic lethality in the context of anticancer therapy.  Nature reviews cancer, 5(9):689–698, 2005. doi: 10 . 1038/nrc1691
  * [22] T. Kranthi, S. Rao, and P. Manimaran.  Identification of synthetic lethal pairs in biological systems through network information centrality.  Molecular bioSystems, 9(8):2163–2167, 2013. doi: 10 . 1039/c3mb25589a
  * [23] A. A. Ku, H.-M. Hu, X. Zhao, K. N. Shah, S. Kongara, D. Wu, F. McCormick, A. Balmain, and S. Bandyopadhyay.  Integration of multiple biological contexts reveals principles of synthetic lethality that affect reproducibility.  Nature communications, 11(1):2375, 2020. doi: 10 . 1038/s41467-020-16078-y
  * [24] M. Lai, G. Chen, H. Yang, J. Yang, Z. Jiang, M. Wu, and J. Zheng.  Predicting synthetic lethality in human cancers via multi-graph ensemble neural network.  In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1731–1734, 2021. doi: 10 . 1109/EMBC46164 . 2021 . 9630716
  * [25] V. Lai, S. Carton, R. Bhatnagar, Q. V. Liao, Y. Zhang, and C. Tan.  Human-ai collaboration via conditional delegation: A case study of content moderation.  In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–18, 2022. doi: 10 . 1145/3491102 . 3501999
  * [26] V. Lai, H. Liu, and C. Tan.  " why is’ chicago’deceptive?" towards building model-driven tutorials for humans.  In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2020. doi: 10 . 1145/3313831 . 3376873
  * [27] V. Lai and C. Tan.  On human predictions with explanations and predictions of machine learning models: A case study on deception detection.  In Proceedings of the conference on fairness, accountability, and transparency, pp. 29–38, 2019. doi: 10 . 1145/3287560 . 3287590
  * [28] J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang.  Biobert: a pre-trained biomedical language representation model for biomedical text mining.  Bioinformatics, 36(4):1234–1240, 2020. doi: 10 . 1093/bioinformatics/btz682
  * [29] J. Li, D. Tang, X. Lu, F. Sun, K. Jiang, and J. Ruan.  Latent space feature representation on multiple biological network for synthetic lethality interaction prediction.  In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1236–1241, 2023. doi: 10 . 1109/BIBM58861 . 2023 . 10385727
  * [30] K. H. Lim, L. M. Ward, and I. Benbasat.  An empirical study of computer system learning: Comparison of co-discovery and self-discovery methods.  Information Systems Research, 8(3):254–272, 1997. doi: 10 . 1287/isre . 8 . 3 . 254
  * [31] G. C. Linderman and S. Steinerberger.  Clustering with t-sne, provably.  SIAM journal on mathematics of data science, 1(2):313–332, 2019. doi: 10 . 1137/18M1216134
  * [32] D. Liu, F. Guo, B. Deng, H. Qu, and Y. Wu.  Egocomp: A node-link-based technique for visual comparison of ego-networks.  Information Visualization, 16, 09 2016. doi: 10 . 1177/1473871616667632
  * [33] L. Liu, X. Chen, C. Hu, D. Zhang, Z. Shao, Q. Jin, J. Yang, H. Xie, B. Liu, M. Hu, et al.  Synthetic lethality-based identification of targets for anticancer drugs in the human signaling network.  Scientific Reports, 8(1):8440, 2018. doi: 10 . 1038/s41598-018-26783-w
  * [34] Z. Liu, Y. Wang, J. Bernard, and T. Munzner.  Visualizing graph neural networks with corgie: Corresponding a graph to its embedding.  IEEE Transactions on Visualization and Computer Graphics, 28(6):2500–2516, 2022. doi: 10 . 1109/TVCG . 2022 . 3148197
  * [35] S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee.  From local explanations to global understanding with explainable ai for trees.  Nature machine intelligence, 2(1):56–67, 2020. doi: 10 . 1038/s42256-019-0138-9
  * [36] D. Luo, W. Cheng, D. Xu, W. Yu, B. Zong, H. Chen, and X. Zhang.  Parameterized explainer for graph neural network.  Advances in neural information processing systems, 33:19620–19631, 2020. doi: 10 . 48550/arXiv . 2011 . 04573
  * [37] S. Ma, M. Sun, and X. Ma.  Modeling adaptive expression of robot learning engagement and exploring its effects on human teachers.  ACM Transactions on Computer-Human Interaction, 30(5):1–48, 2023. doi: 10 . 1145/3571813
  * [38] M. Mackeprang, C. Müller-Birn, and M. T. Stauss.  Discovering the sweet spot of human-computer configurations: A case study in information extraction.  Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–30, 2019. doi: 10 . 1145/3359297
  * [39] L. McInnes and J. Healy.  Umap: Uniform manifold approximation and projection for dimension reduction.  ArXiv, abs/1802.03426, 2018. doi: 10 . 21105/joss . 00861
  * [40] B. J. Oakes, M. Famelis, and H. Sahraoui.  Building domain-specific machine learning workflows: A conceptual framework for the state-of-the-practice.  ACM Trans. Softw. Eng. Methodol., 2023. doi: 10 . 1145/3638243
  * [41] N. J. O’Neil, M. L. Bailey, and P. Hieter.  Synthetic lethality and cancer.  Nature Reviews Genetics, 18(10):613–623, 2017. doi: 10 . 1038/nrg . 2017 . 47
  * [42] C. J. Ryan, L. P. S. Devakumar, S. J. Pettitt, and C. J. Lord.  Complex synthetic lethality in cancer.  Nature Genetics, 55(12):2039–2048, 2023. doi: 10 . 1038/s41588-023-01557-x
  * [43] C. Shan, Y. Shen, Y. Zhang, X. Li, and D. Li.  Reinforcement learning enhanced explainer for graph neural networks.  Advances in Neural Information Processing Systems, 34:22523–22533, 2021.
  * [44] C. Shi, Y. Hu, S. Wang, S. Ma, C. Zheng, X. Ma, and Q. Luo.  Retrolens: A human-ai collaborative system for multi-step retrosynthetic route planning.  In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, article no. 770, 20 pages. Association for Computing Machinery, New York, NY, USA, 2023. doi: 10 . 1145/3544548 . 3581469
  * [45] I. Spinelli, S. Scardapane, and A. Uncini.  A meta-learning approach for training explainable graph neural networks.  IEEE Transactions on Neural Networks and Learning Systems, 2022. doi: 10 . 1109/TNNLS . 2022 . 3171398
  * [46] W. Topatana, S. Juengpanich, S. Li, J. Cao, J. Hu, J. Lee, K. Suliyanto, D. Ma, B. Zhang, M. Chen, et al.  Advances in synthetic lethality for cancer therapy: cellular mechanism and clinical translation.  Journal of hematology & oncology, 13:1–22, 2020. doi: 10 . 1186/s13045-020-00956-5
  * [47] L. van der Maaten and G. E. Hinton.  Visualizing data using t-sne.  Journal of Machine Learning Research, 9:2579–2605, 2008.
  * [48] P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio.  Graph attention networks.  arXiv preprint arXiv:1710.10903, 2017. doi: 10 . 48550/arXiv . 1710 . 10903
  * [49] J. Wang, Q. Zhang, J. cai Han, Y. Zhao, C. Zhao, B. Yan, C. Dai, L. Wu, Y. Wen, Y. Zhang, D. Leng, Z. Wang, X. Yang, S. He, and X. Bo.  Computational methods, databases and tools for synthetic lethality prediction.  Briefings in Bioinformatics, 23, 2022. doi: 10 . 1093/bib/bbac106
  * [50] Q. Wang, K. Huang, P. Chandak, M. Zitnik, and N. Gehlenborg.  Extending the nested model for user-centric xai: A design study on gnn-based drug repurposing.  IEEE Transactions on Visualization and Computer Graphics, 29(1):1266–1276, 2023. doi: 10 . 1109/TVCG . 2022 . 3209435
  * [51] S. Wang, Y. Feng, X. Liu, Y. Liu, M. Wu, and J. Zheng.  Nsf4sl: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers.  Bioinformatics (Oxford, England), 38:ii13–ii19, 09 2022. doi: 10 . 1093/bioinformatics/btac462
  * [52] S. Wang, F. Xu, Y. Li, J. Wang, K. Zhang, Y. Liu, M. Wu, and J. Zheng.  Kg4sl: knowledge graph neural network for synthetic lethality prediction in human cancers.  Bioinformatics, 37(Supplement_1):i418–i425, 2021. doi: 10 . 1093/bioinformatics/btab271
  * [53] X. Wang and R. Simon.  Identification of potential synthetic lethal genes to p53 using a computational biology approach.  BMC medical genomics, 6(1):1–10, 2013. doi: 10 . 1186/1755-8794-6-30
  * [54] X. Wang and M. Yin.  Effects of explanations in ai-assisted decision making: Principles and comparisons.  ACM Trans. Interact. Intell. Syst., 12(4), article no. 27, 36 pages, nov 2022. doi: 10 . 1145/3519266
  * [55] S. Wold, K. Esbensen, and P. Geladi.  Principal component analysis.  Chemometrics and intelligent laboratory systems, 2(1-3):37–52, 1987. doi: 10 . 1016/0169-7439(87)80084-9
  * [56] L.-L. Wu, Y.-Q. Wen, X.-X. Yang, B.-W. Yan, S. He, and X.-C. Bo.  Synthetic lethal interactions prediction based on multiple similarity measures fusion.  Journal of Computer Science and Technology, 36:261–275, 2021. doi: 10 . 1007/s11390-021-0866-2
  * [57] X. Wu, L. Xiao, Y. Sun, J. Zhang, T. Ma, and L. He.  A survey of human-in-the-loop for machine learning.  Future Generation Computer Systems, 135:364–381, 2022. doi: 10 . 1016/j . future . 2022 . 05 . 014
  * [58] C. Yang, Y. Guo, R. Qian, Y. Huang, L. Zhang, J. Wang, X. Huang, Z. Liu, W. Qin, C. Wang, et al.  Mapping the landscape of synthetic lethal interactions in liver cancer.  Theranostics, 11(18):9038, 2021. doi: 10 . 7150/thno . 63416
  * [59] W. Yang, J. Soares, P. Greninger, E. J. Edelman, H. Lightfoot, S. Forbes, N. Bindal, D. Beare, J. A. Smith, I. R. Thompson, et al.  Genomics of drug sensitivity in cancer (gdsc): a resource for therapeutic biomarker discovery in cancer cells.  Nucleic acids research, 41(D1):D955–D961, 2012. doi: 10 . 1093/nar/gks1111
  * [60] Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec.  Gnnexplainer: Generating explanations for graph neural networks.  Advances in neural information processing systems, 32, 2019. doi: 10 . 48550/arXiv . 1903 . 03894
  * [61] X. Yu, D. Liang, and Q. Li.  Improved graphsvx for gnn explanations based on cross entropy.  In 2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI), pp. 147–152. IEEE, 2023. doi: 10 . 1109/ICECAI58670 . 2023 . 10176786
  * [62] H. Yuan, H. Yu, J. Wang, K. Li, and S. Ji.  On explainability of graph neural networks via subgraph explorations.  In International conference on machine learning, pp. 12241–12252. PMLR, 2021. doi: 10 . 48550/arXiv . 2102 . 05152
  * [63] J. Zhang, D. Luo, and H. Wei.  Mixupexplainer: Generalizing explanations for graph neural networks with data augmentation.  In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3286–3296, 2023. doi: 10 . 1145/3580305 . 3599435
  * [64] K. Zhang, M. Wu, Y. Liu, Y. Feng, and J. Zheng.  Kr4sl: knowledge graph reasoning for explainable prediction of synthetic lethality.  Bioinformatics, 39(Supplement_1):i158–i167, 2023. doi: 10 . 1093/bioinformatics/btad261
  * [65] N. Zhang, Z. Bi, X. Liang, S. Cheng, H. Hong, S. Deng, J. Lian, Q. Zhang, and H. Chen.  Ontoprotein: Protein pretraining with gene ontology embedding.  arXiv preprint arXiv:2201.11147, 2022. doi: 10 . 48550/arXiv . 2201 . 11147
  * [66] Y. Zhang, Q. V. Liao, and R. K. E. Bellamy.  Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making.  In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20, 11 pages, p. 295–305. Association for Computing Machinery, New York, NY, USA, 2020. doi: 10 . 1145/3351095 . 3372852
  * [67] Y. Zhang, Q. Yao, L. Yue, X. Wu, Z. Zhang, Z. Lin, and Y. Zheng.  Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.  Nature Computational Science, 3(12):1023–1033, 2023. doi: 10 . 1038/s43588-023-00558-4
  * [68] Z. Zhao, P. Xu, C. Scheidegger, and L. Ren.  Human-in-the-loop extraction of interpretable concepts in deep learning models.  IEEE Transactions on Visualization and Computer Graphics, 28(1):780–790, 2022. doi: 10 . 1109/TVCG . 2021 . 3114837
  * [69] Y. Zhu, Y. Zhou, Y. Liu, X. Wang, and J. Li.  SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network.  Bioinformatics, 39(2):btad015, 01 2023. doi: 10 . 1093/bioinformatics/btad015
  * [70] O. Zuckerman, V. S. Press, E. Barda, B. Megidish, and H. Erel.  Tangible collaboration: A human-centered approach for sharing control with an actuated-interface.  In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–13, 2022. doi: 10 . 1145/3491102 . 3517449