NDLI: Phosphorylation Site Prediction with A Modified k-Nearest Neighbor Algorithm and BLOSUM62 Matrix

Content Provider	IEEE Xplore Digital Library
Author	Ao Li Lirong Wang Yunzhou Shi Minghui Wang Zhaohui Jiang Huanqing Feng
Copyright Year	2005
Description	Author affiliation: Dept. of Electron. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei (Ao Li; Lirong Wang; Yunzhou Shi; Minghui Wang; Zhaohui Jiang; Huanqing Feng)
Abstract	Phosphorylation is one of the most important post-translational modifications for eukaryotic proteins. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is time-consuming and often restricted by the availability of enzymatic reactions. Phosphorylation sites prediction with their specific kinase from machine learning approaches based on their primary sequences is favorably needed, for these methods can provide fast and automatic annotations, which can be used as guidelines for further experimental consideration. In this paper, we presented a modified k-nearest neighbor (k-NN) method measured by the Manhattan distance for phosphorylation site prediction. BLOSUM62-based similarity scores between two phosphorylation sites were adopted as the input vectors. Leave-one-out testing on two PK groups, PKA and CK2, shows that it outperforms two existing methods, Scansite and NetPhosK, which suggests that this method is another competitive computational approach in this branch of bioinformatics
Starting Page	6075
Ending Page	6078
File Size	275993
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Sequences Peptides Biology computing Spatial databases Prediction methods Amino acids Machine learning Guidelines Testing
Content Type	Text
Resource Type	Article

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