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Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network
Yan, Jielu1; Zhang, Bob1; Zhou, Mingliang2; Kwok, Hang Fai3; Siu, Shirley W.I.4,5
2022-08-01
Source PublicationComputers in Biology and Medicine
ISSN0010-4825
Volume147
Abstract

Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

KeywordClassification Deep Learning Drug Discovery Ion Channel Multi-branch-cnn Peptides
DOI10.1016/j.compbiomed.2022.105717
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS SubjectBiology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS IDWOS:000833548400003
Scopus ID2-s2.0-85132782669
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF BIOMEDICAL SCIENCES
Faculty of Health Sciences
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorZhang, Bob; Kwok, Hang Fai; Siu, Shirley W.I.
Affiliation1.PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macao
2.School of Computer Science, Chongqing University, Chongqing, Shapingba, China
3.Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macao
4.Department of Computer and Information Science, University of Macau, Taipa, Macao
5.Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau;  Faculty of Health Sciences
Recommended Citation
GB/T 7714
Yan, Jielu,Zhang, Bob,Zhou, Mingliang,et al. Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network[J]. Computers in Biology and Medicine,2022,147.
APA Yan, Jielu,Zhang, Bob,Zhou, Mingliang,Kwok, Hang Fai,&Siu, Shirley W.I..(2022).Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network.Computers in Biology and Medicine,147.
MLA Yan, Jielu,et al."Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network".Computers in Biology and Medicine 147(2022).
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