DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition

Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convoluti...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	International journal of molecular sciences Ročník 25; číslo 21; s. 11744
Hlavní autoři:	Song, Tao, Song, Haonan, Pan, Zhiyi, Gao, Yuan, Dai, Huanhuan, Wang, Xun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.11.2024 MDPI
Témata:	Algorithms Bioinformatics Computational Biology - methods Datasets Deep Learning DNA methylation Enhancer Elements, Genetic - genetics Gene expression Genetic aspects Genomes Genomics - methods Humans Identification and classification Machine learning Neural networks Neural Networks, Computer Nucleotide sequence Promoter Regions, Genetic Sequence Analysis, DNA - methods Support vector machines China deep learning DNABert enhancer genomic signal
ISSN:	1422-0067, 1661-6596, 1422-0067
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, BiLSTM, for enhancer identification. We first designed the DeepDualEnhancer method based only on the DNA sequence input. It mainly consists of a multi-scale Convolutional Neural Network, and BiLSTM to extract features by DNABert and embedding, respectively. Meanwhile, we collected new datasets from the enhancer–promoter interaction field and designed the method DeepDualEnhancer-genomic for inputting DNA sequences and genomic signals, which consists of the transformer sequence attention. Extensive comparisons of our method with 20 other excellent methods through 5-fold cross validation, ablation experiments, and an independent test demonstrated that DeepDualEnhancer achieves the best performance. It is also found that the inclusion of genomic signals helps the enhancer recognition task to be performed better.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1422-0067 1661-6596 1422-0067
DOI:	10.3390/ijms252111744