Create README.md

README.md

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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
+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
+