A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization

arXiv cs.LG / 3/13/2026

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Key Points

The paper reframes transcription factor binding site prediction as a multi-label classification problem to capture co-binding and cooperative interactions among TFs.
It employs Temporal Convolutional Networks (TCNs) to predict multiple TF binding profiles from DNA sequences, enabling joint learning of TF correlations.
Experimental results indicate that multi-label learning yields reliable predictions and can reveal biologically meaningful motifs and known or novel co-binding patterns.
The work highlights potential biological and practical implications for decoding gene regulation and guiding future experiments with deep learning–based TF binding models.

Abstract

Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most current approaches for TF binding site prediction focus on individual TFs and binary classification tasks, without a full analysis of the possible interactions among various TFs. In this paper we investigate DNA TF binding site recognition as a multi-label classification problem, achieving reliable predictions for multiple TFs on DNA sequences retrieved in public repositories. Our deep learning models are based on Temporal Convolutional Networks (TCNs), which are able to predict multiple TF binding profiles, capturing correlations among TFs andtheir cooperative regulatory mechanisms. Our results suggest that multi-label learning leading to reliable predictive performances can reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, while also suggesting novel relationships and cooperation among TFs.

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A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization

Key Points

Abstract

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