Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.

ABSTRACT: Drug combination therapy is promising for cancer treatment by reducing resistance and improving efficacy. Machine learning approaches to predicting drug combinations require massive training data. Here, we propose Pisces, a novel machine learning approach for drug combination synergy prediction. The key idea is to augment the sparse dataset by creating multiple views for each drug combination based on different modalities. We combined eight modalities of a drug to create 64 augmented views. By treating each augmented view as a separate instance, Pisces can process any number of drug modalities, circumventing the issue of missing modality. Pisces obtained state-of-the-art results on cell-line-based and xenograft-based drug synergy predictions and drug-drug interaction prediction. By interpr

SUBMITTER: Xu H 

PROVIDER: S-EPMC12278649 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature