DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network

Zhang J; Liu B; Wu J; Wang Z; Li J

DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network

dc.citation.issue	1
dc.citation.volume	24
dc.contributor.author	Zhang J
dc.contributor.author	Liu B
dc.contributor.author	Wu J
dc.contributor.author	Wang Z
dc.contributor.author	Li J
dc.coverage.spatial	England
dc.date.accessioned	2024-09-18T02:28:19Z
dc.date.available	2024-09-18T02:28:19Z
dc.date.issued	2023-09-18
dc.description.abstract	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.
dc.description.confidential	false
dc.edition.edition	December 2023
dc.format.pagination	345-
dc.identifier.author-url	https://www.ncbi.nlm.nih.gov/pubmed/37723425
dc.identifier.citation	Zhang J, Liu B, Wu J, Wang Z, Li J. (2023). DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network.. BMC Bioinformatics. 24. 1. (pp. 345-).
dc.identifier.doi	10.1186/s12859-023-05469-9
dc.identifier.eissn	1471-2105
dc.identifier.elements-type	journal-article
dc.identifier.issn	1471-2105
dc.identifier.number	345
dc.identifier.pii	10.1186/s12859-023-05469-9
dc.identifier.uri	https://mro.massey.ac.nz/handle/10179/71477
dc.language	eng
dc.publisher	BioMed Central Ltd
dc.publisher.uri	https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05469-9
dc.relation.isPartOf	BMC Bioinformatics
dc.rights	(c) 2023 The Author/s
dc.rights	CC BY 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Attention mechanism
dc.subject	Bioinformatics
dc.subject	Convolutional neural networks
dc.subject	DNA transcription factors sequence
dc.subject	Transcription Factors
dc.subject	Deep Learning
dc.subject	DNA
dc.subject	Computational Biology
dc.subject	Neural Networks, Computer
dc.title	DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
dc.type	Journal article
pubs.elements-id	480532
pubs.organisational-group	Other