Spectral clustering of protein sequences

被引：0

作者：

Paccanaro, A ^{[1
]}

Chennubhotla, C ^{[1
]}

Casbon, JA ^{[1
]}

Saqi, MAS ^{[1
]}

机构：

[1] Univ London Queen Mary & Westfield Coll, Dept Med Microbiol, Bioinformat Unit, London E1 4NS, England

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A major challenge in bioinformatics is the grouping together of protein sequences into functionally similar families. Large scale clustering of protein sequences may help to identify novel relationships and may also be of use in structural genomics. This paper explores the use of graph-theoretic spectral methods for clustering protein sequences. Using the leading eigenvectors of a matrix derived from similarity information between protein sequences we were able to obtain meaningful clusters on quite diverse sets of proteins. The results presented here show how this method is often able to identify correctly the superfamilies to which the sequences belong.

引用

页码：3083 / 3088

页数：6

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