title: Briefing in application of machine learning methods in ion channel prediction.
abstract: In cells, ion channels are one of the most important classes of membrane proteins which allow inorganic ions to move across the membrane. A wide range of biological processes are involved and regulated by the opening and closing of ion channels. Ion channels can be classified into numerous classes and different types of ion channels exhibit different functions. Thus, the correct identification of ion channels and their types using computational methods will provide in-depth insights into their function in various biological processes. In this review, we will briefly introduce and discuss the recent progress in ion channel prediction using machine learning methods.
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, , Chengdu 610054, China.
authors.forename: Hao
authors.identifier: 0000-0001-6265-2862
authors.initials: H
authors.lastname: Lin
authors.affiliation: Department of Physics, School of Sciences and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China.
authors.forename: Wei
authors.identifier: 0000-0002-6857-7696
authors.initials: W
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030541:Databases, Genetic; D006801:Humans; D007473:Ion Channels / Q000502:physiology*; D000069550:Machine Learning; D008954:Models, Biological; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D007473:Ion Channels