PAIR-Former: Budgeted Relational Multi-Instance Learning for Functional miRNA Target Prediction

TL;DR

PAIR-Former introduces a budgeted relational multi-instance learning framework for miRNA target prediction, balancing relational modeling accuracy with computational efficiency.

Contribution

It formalizes BR-MIL, providing theoretical insights and proposing PAIR-Former, a scalable method that outperforms baselines on biological and non-biological datasets.

Findings

Achieves state-of-the-art F1 scores on miRAW and deepTargetPro datasets.

Scales to large datasets with 420K pairs, outperforming naive approaches.

Demonstrates applicability of BR-MIL beyond biological sequence modeling.

Abstract

Functional miRNA--mRNA targeting is a large-bag prediction problem where each transcript yields a heavy-tailed pool of candidate target sites (CTSs), yet only a pair-level label is observed. Prior methods use max-pooling over individual CTS scores, ignoring relational patterns among sites, but modeling these patterns is critical for accuracy. The challenge is that naive relational aggregation incurs $\mathcal{O}(n^2)$ cost, prohibitive when $n$ reaches thousands, yet a cheap scan alone discards the very interactions that drive functional repression. We formalize this tension as \emph{Budgeted Relational Multi-Instance Learning (BR-MIL)}, a new MIL problem where the compute budget $K$ is a first-class constraint such that at most $K$ instances per bag may receive expensive encoding and relational processing. We establish theoretical foundations for BR-MIL, proving that both approximation quality and generalization are governed by $K$ rather than the raw bag size $n$. Building on this theory, we propose \textbf{PAIR-Former}, which scans all candidates cheaply, selects $K$ diverse CTSs, and aggregates them via Set Transformer. PAIR-Former achieves state-of-the-art performance, outperforming all reproduced baselines with F1$=0.840$ on miRAW (10-fold balanced CV) and $0.839$ on deepTargetPro in transfer evaluation, while achieving $0.793$ on the large-scale MTI benchmark (420K pairs, $38\times$ larger), demonstrating that budgeted relational MIL scales where naive approaches fail. Additional results on CAMELYON16 and Musk2 further show that the proposed BR-MIL formulation extends beyond biological sequence modeling.