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Using Multitask Classification Methods to Investigate ...
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@ARTICLE{GXFF12,
author = {Shan Gao and Shuo Xu and Yaping Fang and Jianwen Fang},
title = {Using Multitask Classification Methods to Investigate the Kinase-Specific
Phosphorylation Sites},
journal = {Proteome Science},
year = {2012},
volume = {10},
pages = {S7},
number = {Suppl. 1},
abstract = {textbf{Background:} Identification 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.
textbf{Methods:} A 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).
textbf{Results:} Using 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.
textbf{Conclusions:} The selected features can be used to build efficient
multitask classifiers with good performance, suggesting they are
important to protein phosphorylation across 4 kinase families.},
}
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