用图神经网络增强序列模型，提升转录因子结合位点预测精度
GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction
- 融合图神经网络与双向LSTM注意力机制，捕捉DNA序列复杂模式
- 在690个数据集上AUC达0.943，165个数据集达0.9495，优于多模态模型
- 适合基因调控研究者，尤其关注精准识别DNA结合位点的场景
转录因子结合位点（TFBS）的识别对理解基因调控至关重要，因这些位点使转录因子能结合DNA并调节基因表达。尽管高通量测序技术发展迅速，但准确识别TFBS仍具挑战，主要因基因组数据庞大且结合模式复杂。本文提出GCBLANE——一种基于图神经网络增强的卷积双向长短期记忆注意力网络，通过整合卷积、多头注意力与循环层，并引入图结构学习，以提取关键特征用于TFBS预测。在690个ENCODE ChIP-Seq数据集上，平均AUC达0.943；在165个ENCODE数据集上，AUC达0.9495，显著优于采用多模态信息（包括DNA形状）的先进模型。结果表明，该方法在序列分析中融入图结构学习，显著提升了预测性能。
原文摘要 · Abstract (English)
Identifying transcription factor binding sites (TFBS) is crucial for understanding gene regulation, as these sites enable transcription factors (TFs) to bind to DNA and modulate gene expression. Despite advances in high-throughput sequencing, accurately identifying TFBS remains challenging due to the vast genomic data and complex binding patterns. GCBLANE, a graph-enhanced convolutional bidirectional Long Short-Term Memory (LSTM) attention network, is introduced to address this issue. It integrates convolutional, multi-head attention, and recurrent layers with a graph neural network to detect key features for TFBS prediction. On 690 ENCODE ChIP-Seq datasets, GCBLANE achieved an average AUC of 0.943, and on 165 ENCODE datasets, it reached an AUC of 0.9495, outperforming advanced models that utilize multimodal approaches, including DNA shape information. This result underscores GCBLANE's effectiveness compared to other methods. By combining graph-based learning with sequence analysis, GCBLANE significantly advances TFBS prediction.
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