GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929762089304064 |
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| author | Ferrao, Jonas Chris Dias, Dickson Morajkar, Sweta Dessai, Manisha Gokuldas Fal |
| author_facet | Ferrao, Jonas Chris Dias, Dickson Morajkar, Sweta Dessai, Manisha Gokuldas Fal |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_12377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction Ferrao, Jonas Chris Dias, Dickson Morajkar, Sweta Dessai, Manisha Gokuldas Fal Machine Learning Genomics Quantitative Methods 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. |
| title | GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction |
| topic | Machine Learning Genomics Quantitative Methods |
| url | https://arxiv.org/abs/2503.12377 |