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Kinase Identification with Supervised Laplacian Regularized Least Squares
- Source :
- PLoS ONE, PLoS ONE, Vol 10, Iss 10, p e0139676 (2015)
- Publication Year :
- 2015
-
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.
- Subjects :
- Protein Kinase C-alpha
Computer science
lcsh:Medicine
Mass spectrometry
Bioinformatics
Protein sequencing
Least-Squares Analysis
Phosphorylation
lcsh:Science
Multidisciplinary
business.industry
Kinase
lcsh:R
Pattern recognition
Parameter identification problem
Identification (information)
Kernel method
ROC Curve
Kernel (statistics)
Area Under Curve
lcsh:Q
Artificial intelligence
business
Laplace operator
Protein Kinases
Algorithms
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 10
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....326dec2c4a639afe4c16eac6665cd490