iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network

Abstract

DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

“…Compared with conventional clinical calculation equations ( Chiu et al, 2005 ), the ANNs obtained better results. Deep learning ( Dao et al, 2021 ; Lv et al, 2021a , b ) also made a great contribution to the clinic, including skin cancer ( Esteva et al, 2017 ), breast cancer ( Liu J. et al, 2021 ), and brain diseases ( Liu G. et al, 2018 ; Liu et al, 2019 ; Bi et al, 2020 ; Hu et al, 2020 , 2021a , b ). In biological field, machine learning has been widely used to solve biological problems, including O -GlcNAcylation site prediction ( Jia et al, 2018 ), microbiology analysis ( Qu et al, 2019 ), microRNAs and cancer association prediction ( Zeng et al, 2018 ), lncRNAs ( Cheng et al, 2016 ; Deng et al, 2021 ), CircRNAs ( Fang et al, 2019 ; Zhao et al, 2019 ), DNA methylation site ( Wei et al, 2018b ; Zou et al, 2019 ; Dai et al, 2020 ), osteoporosis diagnoses ( Su et al, 2020b ), function prediction of proteins ( Wei et al, 2018a ; Wang H. et al, 2019 ; Deng et al, 2020b ; Ding et al, 2020a ; Su et al, 2020a ), nucleotide binding sites ( Ding et al, 2021b ), drug complex network analysis ( Ding et al, 2019 , 2020b , a ; Deng et al, 2020a ; Han et al, 2021 ; Liu H. et al, 2021 ), protein remote homology ( Liu B. et al, 2018 ), electron transport proteins ( Ru et al, 2019 ), and cell-specific replication.…”

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

“…In the future, we will stay focused on the H. pylori membrane protein prediction issues and screen the possible vaccine candidates and drug targets. Moreover, we will collect more data to train a deep learning model [66][67][68][69][70][71] to improve prediction performance.…”

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

“…Deep learning is also a popular method in bioinformatics ( Dao et al, 2021a , b ; Lv H. et al, 2021 ; Wang et al, 2021 ; Zulfiqar et al, 2022 ). MLP is a feed-forward neural network containing input, hidden, and output layers for receiving input data, processing data, and performing final prediction, respectively.…”