Open AccessAn information-based network approach for protein classification
Author(s) -
Xiaogeng Wan,
Xiaohui Zhao,
Stephen S.-T. Yau
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0174386
Subject(s) - computer science , multivariate statistics , artificial intelligence , pattern recognition (psychology) , binary number , binary classification , computational biology , protein structure , binary tree , tree (set theory) , protein interaction networks , data mining , machine learning , bioinformatics , protein–protein interaction , mathematics , biology , algorithm , combinatorics , support vector machine , genetics , biochemistry , arithmetic
Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method.