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PLoS One. 2015 Oct 8;10(10):e0139676. doi: 10.1371/journal.pone.0139676. eCollection 2015.

Kinase Identification with Supervised Laplacian Regularized Least Squares.

Author information

1
School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, Anhui, China; Centers for Biomedical Engineering, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, Anhui, China.
2
School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, Anhui, China.

Abstract

Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms.

PMID:
26448296
PMCID:
PMC4598036
DOI:
10.1371/journal.pone.0139676
[Indexed for MEDLINE]
Free PMC Article

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