{rfName}
De

Indexed in

License and use

Citations

Altmetrics

Analysis of institutional authors

Song T.Author

Share

Publications
>
Article

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

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

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

Affiliations

Abstract

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.

Keywords

AlgorithmAlgorithmsArticleArtificial neural networkBioinformaticsComputational biologyControlled studyConvolutional neural networkCross validationDeep learningDna sequenceDna sequencingDnabertEnhancerEnhancer elements, geneticEnhancer regionGeneticsGenomic signalGenomicsHumanHumansNeural networks, computerNonhumanProceduresPromoter regionPromoter regions, geneticRegulatory dna sequenceSequence analysis, dna

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal Of Molecular Sciences due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 68/231, thus managing to position itself as a Q1 (Primer Cuartil), in the category Chemistry, Multidisciplinary.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-03:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 6 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: China.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (SONG, TAO) .