Predicts lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. Compared with the existing predictors in this area, pSuc-Lys can achieve remarkably higher success rates. For the convenience of most experimental scientists, we have provided its web-server and a step-by-step guide, by which users can easily obtain their desired results without the need to go through the mathematical formulations.
Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; Computer science, University of Birmingham, UK; Gordon Life Science Institute, Boston, MA, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
pSuc-Lys funding source(s)
This work was partially supported by the National Natural Science Foundation of China (Nos. 61261027, 31260273, 31560316, 31560316), the Natural Science Foundation of Jiangxi Province, China (No. 20122BAB211033, 20122BAB201044, 20132BAB201053), the Scientific Research plan of the Department of Education of JiangXi Province (GJJ14640).