Indexado en

Licencia y uso

Citaciones

1

CITATION

Altmetrics

PlumX Metrics

• Capturas
  • Lectores: 5
• Menciones
  • Menciones Blog: 1
  • Menciones Noticias: 1

Ver detalles

Análisis de autorías institucional

Compartir

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

Publicado en:International Journal Of Molecular Sciences. 25 (21): - 2024-01-01 25(21), DOI: 10.3390/ijms252111744

Autores: Song T; Song H; Pan Z; Gao Y; Dai H; Wang X

Afiliaciones

Resumen

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. © 2024 by the authors.

Palabras clave