| | --- |
| | license: afl-3.0 |
| | tags: |
| | - biology |
| | --- |
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| | FLIP SCL Dataset: SubCellular Localization |
| | SCL = SubCellular Localization |
| | What It Is |
| | This is a multi-label classification task where the goal is to predict which cellular compartment(s) a protein resides in within eukaryotic cells. |
| | The 10 Subcellular Locations |
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| | Note: DROP any example that has split=nan when training. The reason for leaving them is to keep this dataset identical to the original one. |
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| | Based on the DeepLoc dataset that FLIP appears to be derived from: |
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| | Nucleus |
| | Cytoplasm |
| | Extracellular (secreted proteins) |
| | Mitochondrion |
| | Cell membrane |
| | Endoplasmic reticulum (ER) |
| | Chloroplast (plants only) |
| | Golgi apparatus |
| | Lysosome/Vacuole |
| | Peroxisome |
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| | Why Multi-Label? |
| | Some proteins localize to multiple compartments (e.g., shuttling between nucleus and cytoplasm, or dual-targeted to mitochondria and chloroplasts). This is biologically important - around 20-30% of proteins have multiple localizations. |
| | The Splits Explained |
| | Looking at the directory structure, FLIP provides these splits: |
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| | 1. balanced.csv |
| | Standard split with balanced representation across localization categories |
| | Avoids class imbalance issues |
| | Likely uses random or cluster-based splitting |
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| | 2. human_hard.csv / human_soft.csv |
| | Human-only proteins (Homo sapiens) |
| | Hard split: More challenging - possibly using lower sequence identity cutoff between train/test, or testing on rare/underrepresented localizations |
| | Soft split: Easier - higher sequence similarity allowed between train/test sets |
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| | 3. mixed_hard.csv / mixed_soft.csv |
| | Cross-species proteins (eukaryotes: yeast, plants, animals, fungi) |
| | Hard split: Tests generalization across phylogenetically distant organisms or rare localization patterns |
| | Soft split: Tests on more similar proteins/organisms |
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| | What Makes Splits "Hard" vs "Soft"? |
| | Based on subcellular localization literature: |
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| | Soft splits likely have: |
| | Higher sequence identity between train/test (e.g., 30-40% homology allowed) |
| | Similar organisms in train/test |
| | Balanced representation of all localization types |
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| | Hard splits likely have: |
| | Lower sequence identity between train/test (e.g., <20-30% homology) |
| | Phylogenetic distance: Train on yeast/bacteria, test on human |
| | Rare localizations: Test on underrepresented compartments (peroxisome, chloroplast) |
| | Multi-localization complexity: Test on proteins with multiple localizations |
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| | Key Challenge for FLIP |
| | Unlike other FLIP tasks that test extrapolation in fitness values (e.g., low→high fitness), SCL tests: |
| | Sequence-based generalization: Can models predict localization for proteins unlike any in training? |
| | Multi-label complexity: Handling proteins that go to 2-3 compartments |
| | Cross-species transfer: Learning universal localization rules vs. species-specific signals |
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