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

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 conditions. Three cross-domain findings emerge: (1) inductive transfer succeeds in biology--a node-disjoint split with unseen genes yields AUC 0.826 (Delta +0.127)--where it fails in text (+/-0.10), suggesting physically grounded associations are more transferable than contingent co-occurrences; (2) CAL scores anti-correlate with interaction degree (Spearman r = -0.590), with gains concentrating on understudied genes with focused interaction profiles; (3) tighter association quality outperforms larger but noisier training sets, reversing the text pattern. Results are stable across training seeds (SD < 0.001) and cross-boundary threshold choices.

Figures (5)

• Figure 1: Cross-boundary AUC across all four experiments. CAL provides large gains on gene perturbation data where latent associative signal exists (Replogle, DepMap). Drug experiments reveal failure modes: no latent signal (fingerprints) and degree confounding (L1000, where shuffled $>$ real).
• Figure 2: Confidence threshold sweep showing quality-over-quantity. (A) Overall AUC and (B) cross-boundary AUC both improve with tighter thresholds despite fewer training pairs. Experimental-only filtering (hatched) matches the highest combined-score threshold.
• Figure 3: Cross-boundary improvement by degree quintile (high confidence $\geq$900, cross-boundary pairs with $|\text{cosine}| < 0.2$). Low-degree genes (Q1) benefit approximately twice as much as high-degree genes (Q5). The trend is approximately monotonic; Q4 and Q5 show near-equal improvement (+0.124 vs +0.127), suggesting the degree effect saturates for high-degree genes. Table \ref{['tab:quintile']} shows Q1 and Q5 from the same high-confidence cross-boundary data; the appendix (Table \ref{['tab:degree_full']}) uses medium-confidence all-pairs.
• Figure 4: (A) Association score distributions by cosine similarity bin. In the cross-boundary regime (yellow shading), STRING-positive pairs (blue) show clear separation from non-associated pairs (grey). (B) Three illustrative gene pairs showing CAL lift: each arrow indicates the gain from cosine (grey dot on diagonal) to the actual association score (blue dot).
• Figure 5: Cross-boundary sensitivity analysis. As the threshold tightens (left to right), cosine AUC declines toward chance while CAL AUC remains stable or slightly increases. Shaded region shows the growing gap.