GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction

• URL: http://arxiv.org/abs/2503.12377v1
• Date: Sun, 16 Mar 2025 06:52:03 GMT
• Title: GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction
• Authors: Jonas Chris Ferrao, Dickson Dias, Sweta Morajkar, Manisha Gokuldas Fal Dessai,
• Abstract summary: GCBLANE is a graph-enhanced convolutional bidirectional Long Short-Term Memory (LSTM) attention network.<n>It integrates convolutional, multi-head attention, and recurrent layers with a graph neural network to detect key features for TFBS prediction.<n>On 690 ENCODE ChIP-Seq datasets, GCBLANE achieved an average AUC of 0.943, and on 165 ENCODE, it reached an AUC of 0.9495.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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