Topic
Graph neural networks research, explained
12 open-access graph neural networks studies, each with a flashcard deck and a quiz.
- Can spreading signals through a protein network find disease genes?
Spreading known disease-gene information smoothly across the whole protein interaction network ranked the true causal gene first more often than earlier network methods.
- Can a known gene network make microarray classifiers interpretable?
Forcing a classifier's gene weights to vary smoothly across a known metabolic network kept accuracy unchanged while making the classifier readable in terms of pathways.
- Can a graph neural network sort unknown phage DNA into families?
Linking DNA fragments into a graph by shared proteins and training a graph network on labelled and unlabelled fragments together classified novel phages more accurately than existing tools, especially for short fragments.
- Can self-supervised learning predict how mutations change binding?
A graph network pretrained to repair perturbed protein structures predicted mutation effects on binding better than energy-based and feature-engineered methods, even on structures it had not seen.
- Does letting each node choose its own depth fix GNN over-smoothing?
Letting each drug or protein node decide how far to gather information from its neighbours kept graph features distinct and slightly improved drug-target prediction.
- Can a graph neural network judge predicted protein shapes?
Treating a protein as a graph of residues and contacts let a fairly simple neural network rank predicted protein structures about as well as the best existing single-model tools.
- Does background biological knowledge help predict deadly gene pairs?
Feeding a graph neural network a knowledge graph of pathways, diseases and drugs improved prediction of synthetic-lethal gene pairs compared with methods that only saw known gene pairs.
- Can a neural network predict which cancer drugs work on which cells?
Combining a graph network for drug structure with gene features predicts drug sensitivity well on familiar cell lines, but error more than doubles on cell lines never seen in training.
- Can predicted protein shapes help find where antibodies bind?
Feeding AlphaFold2-predicted 3D structures into a graph neural network made antibody-binding-site prediction clearly better than sequence-only and earlier structure-based tools.
- Can an equivariant graph network judge protein models better?
A graph network built to respect 3D rotations and translations estimated the quality of predicted protein structures more accurately than earlier methods, and even better than AlphaFold2's own confidence scores.
- Can graph networks on 3D protein shapes predict drug binding?
Representing both drugs and AlphaFold-predicted proteins as graphs, and letting the two interact through an attention-like module, predicted drug-target binding better than four earlier deep-learning methods.
- Does building 3D symmetry into a GNN improve protein site prediction?
A graph neural network whose outputs transform correctly under rotations, reflections and translations predicted protein interaction sites better than earlier methods, even from AlphaFold2-predicted structures.