AUTHOR=Jia Jianhua , Lei Rufeng , Qin Lulu , Wu Genqiang , Wei Xin 

TITLE=iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module

JOURNAL=Frontiers in Genetics

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1132018

DOI=10.3389/fgene.2023.1132018

ISSN=1664-8021

ABSTRACT=Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts a lot of emphases on predicting enhancers and their strength. It is vital to create quick and accurate calculating techniques since the conventional biomedical tests take too long time and are too expensive. This paper proposed a new predictor called iEnhancer-DCSV that is built on a modified Densely Connected Convolutional Networks (DenseNet) and an improved Convolutional Block Attention Module(CBAM). The Coding was done using One-hot and Nucleotide Chemical Property (NCP). DenseNet was used to extract advanced features from raw coding. The channel attention and spatial attention modules was used to evaluate the significance of the advanced features and then input to a fully connected neural network to yield the prediction probabilities. Finally, ensemble learning was employed on the final categorization findings via voting. According to the experimental results on the test set, the first layer of enhancer recognition achieved an accuracy of 78.95%, and the Matthews correlation coefficient value was 0.5809. The second layer of enhancer strength prediction achieved an accuracy of 80.70%, and the Matthews correlation coefficient value was 0.6609. The iEnhancer-DCSV method can be found at https://github.com/leirufeng/iEnhancer-DCSV. It is easy to get their desired results without needing to use the complicated mathematical equations involved.