Protein Biological Function Clustering with GO and KO

Author

Zhou, Meichen ; Zhu, Fei

Author_Institution

Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China

fYear

2011

fDate

10-12 May 2011

Firstpage

1

Lastpage

4

Abstract

With the completion of human genome sequencing, the research on protein structure and function is a hot area of genomics research. Besides, one of the Bioinformatics key issues is to understand the meaning and function of proteins which are defined by genes from the chromosomes. Clustering of proteins is an effective way to solve this problem. In Biology, GO is the most famous Bio-ontology. It has three independent kinds of ontology: biological process, molecular function and cellular component. We use biological process ontology to cluster the proteins. In addition, KO is a protein classification system in KEGG. With the help of KO database, we determine more clearly whether the proteins belong to the same sort. With GO and KO, we use a new Adaptive Sectional Set Feature-weighted Fuzzy C-means algorithm to cluster protein biological function.

Keywords

bioinformatics; biology computing; cellular biophysics; fuzzy set theory; genomics; ontologies (artificial intelligence); pattern classification; pattern clustering; proteins; GO; KEGG orthology; KO; KO database; adaptive sectional set feature weighted fuzzy c-means algorithm; bioinformatics; biological process ontology; bioontology; cellular component; chromosome; gene ontology; genomics research; human genome sequencing; protein biological function clustering; protein classification system; protein structure; Bioinformatics; Biological processes; Clustering algorithms; Databases; Protein engineering; Proteins;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on

Conference_Location

Wuhan

ISSN

2151-7614

Print_ISBN

978-1-4244-5088-6

Type

conf

DOI

10.1109/icbbe.2011.5779983

Filename

5779983