Graph Convolutional Matrix Factorization #442
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Graph-convolutional matrix factorization for drug-response prediction. Predicts the
cell-line x drug response matrix as R = U V^T (as in ordinary matrix factorization),
but the latent factors are learned end-to-end by graph convolutions over
feature-similarity or prior-knowledge graphs (k-NN gene-expression graph for cell
lines, Morgan-fingerprint Tanimoto graph for drugs). Each convolution smooths a node's
embedding over its graph neighbours; the smoothed factors are read out by the dot
product, plus per-cell/per-drug biases and an optional MLP head.
Four selectable models, all registered in MULTI_DRUG_MODEL_FACTORY:
* GCMF - base single-graph model.
* RGCMF - relational/multi-graph variant (multi-omics cell graphs; pathway and
bioassay drug relations) fused by a relational graph convolution.
Works best on leave-cell-out in our experiments.
* PGCMF / PRGCMF - probabilistic variants with a heteroscedastic Gaussian-NLL head
emitting calibrated per-prediction aleatoric uncertainty.
Includes hyperparameters.yaml for all four, bundled gzipped drug-relation resources
for RGCMF/PRGCMF, smoke tests (train/predict/save-load round-trip), docs (model page
+ toctree + usage table), and a check-added-large-files exclude for the bundled
resources.
Lint/type clean: flake8 0, mypy 0, black/isort clean, pre-commit green.
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Graph convolutional matrix factorization for drug-response prediction. Predicts the cell-line x drug response matrix as R = U V^T (as in normal matrix fact.) But the latent factors are learned end2end by graph convolutions over feature-similarity or prior knowledge graphs. One can use mutiple graphs for drugs and cell lines in the relational gcmf version.
The P-versions also produce aleatoric uncertainty estimates.
The main idea is to combine learning in primal and dual space (one can use a large-dimensional feature space and transform it to a similarity, and exploit it without blowing up the model complexity). Also, the graph convolution smooths the embeddings with those of the neighboring cells, which I hope prevents overfitting/helps generalization.
The variants are
bioassay drug relations) fused by a relational graph convolution. (works best on LCO per my experiments)
head emitting calibrated per-prediction uncertainty.
Per my experiments this is chocolate worthy but will rerun on the leaderboard scripts.
I will remove the csvs etc, just wanted to backup here