Protein annotation from protein interaction networks and Gene Ontology

被引：21

作者：

Nguyen, Cao D. ^{[1
,2
]}

Gardiner, Katheleen J. ^{[3
,4
,5
]}

Cios, Krzysztof J. ^{[2
,6
]}

机构：

[1] Univ Western Australia, Med Res Ctr, Nedlands, WA 6009, Australia

[2] Virginia Commonwealth Univ, Richmond, VA 23284 USA

[3] Univ Colorado Denver, Dept Pediat, Intellectual & Dev Disabil Res Ctr, Program Computat Biol, Denver, CO USA

[4] Univ Colorado Denver, Dept Pediat, Intellectual & Dev Disabil Res Ctr, Program Neurosci, Denver, CO USA

[5] Univ Colorado Denver, Dept Pediat, Intellectual & Dev Disabil Res Ctr, Program Human Med Genet, Denver, CO USA

[6] Polish Acad Sci, Inst Theoret & Appl Informat, PL-00901 Warsaw, Poland

来源：

JOURNAL OF BIOMEDICAL INFORMATICS | 2011年 / 44卷 / 05期

基金：

美国国家卫生研究院;

关键词：

Protein function; Protein interaction networks; Naive Bayes; Association rules; Gene Ontology; INTERACTION MAP; FUNCTION PREDICTION; FUNCTIONAL MODULES; HETEROGENEOUS DATA; GENOME; DATABASE; ALGORITHM;

D O I：

10.1016/j.jbi.2011.04.010

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We introduce a novel method for annotating protein function that combines Naive Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for chi(2)-statistics, respectively. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：824 / 829

页数：6

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