Using multitask classification methods to investigate the kinase-specific phosphorylation sites

Proteome Science

Proceedings

Shan Gao¹  Yaping Fang¹  Jianwen Fang¹  Shuo Xu² 

[1] Applied Bioinformatics Laboratory, Kansas University, 2034 Becker Dr., 66047, Lawrence, KS, USA;Institute of Scientific and Technical Information of China, No. 15 Fuxing Road, Haidian District, 100038, Beijing, P.R. China;

关键词: Support Vector Machine;    Feature Selection;    Window Size;    Random Forest;    Phosphorylation Site;   

DOI  :  10.1186/1477-5956-10-S1-S7

来源: Springer

【 摘 要 】

BackgroundIdentification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.MethodsA multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).ResultsUsing the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.ConclusionsThe selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.

【 授权许可】

Unknown   
© Gao et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.