Predicts self-interacting proteins (SIPs) based on protein amino acids sequence. RVMBIGP is based on the Relevance Vector Machine (RVM) model combined with Bi-gram probability (BIGP). It can characterize the subsequence of amino acids in the conserved regions and capture the useful evolutionary information. The tool is capable to produce high accuracies of 95.48% and 98.80% on yeast and human datasets.
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China; School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China; School of Electronics and Information Engineering, Tongji University, Shanghai, China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
RVMBIGP funding source(s)
Supported in part by the National Natural Science of Foundation of China under Grant 61373086, 61572506, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences; by the National Natural Science of Foundation of China under Grant 11301517 and National Center for Mathematics and Interdisciplinary Sciences, CAS.