@article{yan_multi-branch-cnn_2022, title = {Multi-{Branch}-{CNN}: {Classification} of ion channel interacting peptides using multi-branch convolutional neural network}, volume = {147}, issn = {0010-4825}, shorttitle = {Multi-{Branch}-{CNN}}, url = {https://www.sciencedirect.com/science/article/pii/S0010482522004954}, doi = {10.1016/j.compbiomed.2022.105717}, 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.}, language = {en}, urldate = {2022-09-21}, journal = {Computers in Biology and Medicine}, author = {Yan, Jielu and Zhang, Bob and Zhou, Mingliang and Kwok, Hang Fai and Siu, Shirley W. I.}, month = aug, year = {2022}, note = {3 citations (Crossref) [2022-09-21]}, keywords = {Classification, Deep learning, Drug discovery, Ion channel, Multi-Branch-CNN, Peptides}, pages = {105717}, } @article{yan_recent_2022, title = {Recent {Progress} in the {Discovery} and {Design} of {Antimicrobial} {Peptides} {Using} {Traditional} {Machine} {Learning} and {Deep} {Learning}}, volume = {11}, copyright = {http://creativecommons.org/licenses/by/3.0/}, issn = {2079-6382}, url = {https://www.mdpi.com/2079-6382/11/10/1451}, doi = {10.3390/antibiotics11101451}, abstract = {Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.}, language = {en}, number = {10}, urldate = {2022-11-09}, journal = {Antibiotics}, author = {Yan, Jielu and Cai, Jianxiu and Zhang, Bob and Wang, Yapeng and Wong, Derek F. and Siu, Shirley W. I.}, month = oct, year = {2022}, note = {Number: 10 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {antimicrobial peptide, classification, deep learning, machine learning, medicine, regression, therapeutic peptide}, pages = {1451}, }