Datasets:
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license: afl-3.0
tags:
- biology
---
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
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.
Based on the DeepLoc dataset that FLIP appears to be derived from:
Nucleus
Cytoplasm
Extracellular (secreted proteins)
Mitochondrion
Cell membrane
Endoplasmic reticulum (ER)
Chloroplast (plants only)
Golgi apparatus
Lysosome/Vacuole
Peroxisome
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:
1. balanced.csv
Standard split with balanced representation across localization categories
Avoids class imbalance issues
Likely uses random or cluster-based splitting
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
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
What Makes Splits "Hard" vs "Soft"?
Based on subcellular localization literature:
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
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
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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