A lysine acetylation site prediction system based on a logistic regression model. In practice, the amino acid sequence of the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids were utilized as features of LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets.
School of Biological Engineering, East China University of Science and Technology, Shanghai, China; Shanghai Center for Bioinformation Technology, Shanghai, China; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
LAceP funding source(s)
This work was supported by grants from the National Natural Science Foundation of China (61272250, .31100957), the National Basic Research Program of China (2013CB956103), SA-SIBS Scholarship Program, and the National High-Tech R&D Program (863) (2012AA101601).