PAIR-Former: Budgeted Relational MIL for miRNA Target Prediction

• URL: http://arxiv.org/abs/2602.00465v1
• Date: Sat, 31 Jan 2026 02:39:23 GMT
• Title: PAIR-Former: Budgeted Relational MIL for miRNA Target Prediction
• Abstract summary: We propose a BR-MIL pipeline that performs a cheap full-pool scan, selects up to $K$ diverse CTSs on CPU, and applies a permutation-invariant Set Transformer on the selected tokens.<n>On miRAW, PAIR-Former outperforms strong pooling baselines at a practical operating budget.
• Score: 1.6791044863781392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Functional miRNA--mRNA targeting is a large-bag prediction problem: each transcript yields a heavy-tailed pool of candidate target sites (CTSs), yet only a pair-level label is observed. We formalize this regime as \emph{Budgeted Relational Multi-Instance Learning (BR-MIL)}, where at most $K$ instances per bag may receive expensive encoding and relational processing under a hard compute budget. We propose \textbf{PAIR-Former} (Pool-Aware Instance-Relational Transformer), a BR-MIL pipeline that performs a cheap full-pool scan, selects up to $K$ diverse CTSs on CPU, and applies a permutation-invariant Set Transformer aggregator on the selected tokens. On miRAW, PAIR-Former outperforms strong pooling baselines at a practical operating budget ($K^\star{=}64$) while providing a controllable accuracy--compute trade-off as $K$ varies. We further provide theory linking budgeted selection to (i) approximation error decreasing with $K$ and (ii) generalization terms governed by $K$ in the expensive relational component.

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