DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
Open accessAbstract
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
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CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis.
“Recently, the CNN has been widely applied in various medical imaging, laboratory reports, pathology reports, etc., and has achieved remarkable success in the field of AI-based diagnosis [51,52,53,54,55,56,57,58].”
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- Authors
- Zhang, J., Liu, B., Wu, J., Wang, Z., & Li, J.
- Affiliations
- Beijing University of Technology (CN)
- Massey University (NZ)
- Corresponding author
- Liu, Bo
- Venue
- BMC Bioinformatics
- Published
- 2023-09-18
- Volume
- 24
- Issue
- 1
- Pages
- 345-345
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC10506269
- OpenAlex
- W4386835737
- Cited by
- 15
- References
- 42
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 1
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62076015)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- multi-head self-attention, convolutional neural networks, parameter-efficient models, protein sequence classification, gene expression, long-range dependencies
- MeSH terms
- Deep Learning, DNA, Transcription Factors, Neural Networks, Computer, Computational Biology
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