用多标签时序卷积模型预测多个转录因子协同结合规律
A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
- 将转录因子结合预测建模为多标签分类任务，利用时序卷积网络捕捉协同作用
- 在公开数据集上实现多个转录因子结合位点的可靠预测，识别出已知与新发现的协同模式
- 适合生物信息学、基因调控研究者关注转录因子互作机制
转录因子（TFs）通过复杂且协同的机制调控基因表达。尽管多个转录因子常共同作用，但其结合逻辑及相互作用机制尚未完全阐明。当前多数转录因子结合位点预测方法聚焦于单个转录因子的二分类任务，缺乏对不同转录因子间潜在交互关系的系统分析。本文将DNA转录因子结合位点识别视为多标签分类问题，在公共数据库获取的DNA序列上实现多个转录因子的可靠预测。所提出的深度学习模型基于时序卷积网络（TCNs），可同时预测多个转录因子的结合图谱，捕捉转录因子间的相关性及其协同调控机制。结果表明，多标签学习不仅实现了可靠的预测性能，还揭示了与已知转录因子互作一致的生物学有意义基序和共结合模式，同时提示了新的转录因子合作关系。
原文摘要 · Abstract (English)
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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