[2603.20955] Beyond Expression Similarity: Contrastive Learning Recovers Functional Gene Associations from Protein Interaction Structure

Computer Science > Machine Learning arXiv:2603.20955 (cs) [Submitted on 21 Mar 2026] Title:Beyond Expression Similarity: Contrastive Learning Recovers Functional Gene Associations from Protein Interaction Structure Authors:Jason Dury View a PDF of the paper titled Beyond Expression Similarity: Contrastive Learning Recovers Functional Gene Associations from Protein Interaction Structure, by Jason Dury View PDF HTML (experimental) Abstract:The Predictive Associative Memory (PAM) framework posits that useful relationships often connect items that co-occur in shared contexts rather than items that appear similar in embedding space. A contrastive MLP trained on co-occurrence annotations--Contrastive Association Learning (CAL)--has improved multi-hop passage retrieval and discovered narrative function at corpus scale in text. We test whether this principle transfers to molecular biology, where protein-protein interactions provide functional associations distinct from gene expression similarity. Four experiments across two biological domains map the operating envelope. On gene perturbation data (Replogle K562 CRISPRi, 2,285 genes), CAL trained on STRING protein interactions achieves cross-boundary AUC of 0.908 where expression similarity scores 0.518. A second gene dataset (DepMap, 17,725 genes) confirms the result after negative sampling correction, reaching cross-boundary AUC of 0.947. Two drug sensitivity experiments produce informative negatives that sharpen boundary conditio...