With the development of high-throughput sequencing techniques, more and more sequencing data is available, including genomic reads, transcriptome data, and proteomic sequences. In order to fully utilize this information, it is critical to also investigate underlying mechanisms of biological function. Usually, researchers need to distinguish, or cluster, the sequence data from proteomic and genomic functional analyses. Genomic functions can also be identified from classification results, such as motif and regulatory region identification, and even some epigenomics and disease relationship predictions.
Machine learning methods are important techniques for these tasks, especially for ensemble learning, large scale data processing, various kernel designs, and imbalanced classification methods.
We invite authors to contribute original research manuscripts or reviews, which introduce advanced machine learning algorithms and their application in protein or genome sequence analysis.
Potential topics include, but are not limited to:
● Protein structure prediction with machine learning methods
● Special protein identification methods
● Epigenomics and disease relationship prediction
● Motif and regulatory element(s) identification from high-throughput data
● Advanced machine learning methods with the application to bioinformatics
● Cloud computing and parallel machine learning techniques for protein structure and genomics function analysis
Prof. Dr. Leyi Wei
Manuscripts should be submitted via our online editorial system at https://imr.propub.com by registering and logging in to this website. Once you are registered, click here to start your submission. Manuscripts can be submitted now or up until the deadline. All papers will go through peer-review process. Accepted papers will be published in the journal (as soon as accepted) and meanwhile listed together on the special issue website. Research articles, reviews as well as short communications are preferred. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office to announce on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts will be thoroughly refereed through a double-blind peer-review process. Please visit the Instruction for Authors page before submitting a manuscript. The Article Processing Charge (APC) in this open access journal is 2500 USD. Submitted manuscripts should be well formatted in good English.
- Open Access Original ResearchAnnotating whole genome variants and constructing a multi-classifier based on samples of ADNIJuan Zhou, Yangping Qiu, Xiangyu Liu, Ziruo Xie, ... Xiong LiFront. Biosci. (Landmark Ed) 2022, 27(1), 37; https://doi.org/10.31083/j.fbl2701037(This article belongs to the Special Issue Machine Learning Techniques for High-Throughput Function Analysis in Proteomics and Genomics)107Downloads1Citations310Views
- Open Access Original ResearchComprehensive evaluation of protein-coding sORFs prediction based on a random sequence strategyJiafeng Yu, Li Guo, Xianghua Dou, Wenwen Jiang, ... Congmin XuFront. Biosci. (Landmark Ed) 2021, 26(8), 272–278; https://doi.org/10.52586/4943(This article belongs to the Special Issue Machine Learning Techniques for High-Throughput Function Analysis in Proteomics and Genomics)29Downloads2Citations90Views
- Open Access Original ResearchImproved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary patternZheng-Yang Zhao, Wen-Zhun Huang, Xin-Ke Zhan, Yu-An Huang, ... Chang-Qing YuFront. Biosci. (Landmark Ed) 2021, 26(7), 222–234; https://doi.org/10.52586/4936(This article belongs to the Special Issue Machine Learning Techniques for High-Throughput Function Analysis in Proteomics and Genomics)8Downloads43Views