Systematic computational prediction of protein interaction networks

被引：27

作者：

Lees, J. G. ^{[1
]}

Heriche, J. K. ^{[2
]}

Morilla, I. ^{[3
]}

Ranea, J. A. ^{[1
,3
]}

Orengo, C. A. ^{[1
]}

机构：

[1] UCL, Res Dept Struct & Mol Biol, London, England

[2] European Mol Biol Lab, Cell Biol Biophys Unit, D-69117 Heidelberg, Germany

[3] Univ Malaga, Dept Mol Biol & Biochem CIBER Enfermedades Raras, E-29071 Malaga, Spain

来源：

PHYSICAL BIOLOGY | 2011年 / 8卷 / 03期

关键词：

DATA INTEGRATION METHODOLOGY; GENOME CONTEXT METHODS; FUNCTIONAL LINKAGES; GENE ONTOLOGY; DOMAIN-DOMAIN; EVOLUTIONARY CONSERVATION; ESCHERICHIA-COLI; INTERACTION MAP; YEAST; COMPLEXES;

D O I：

10.1088/1478-3975/8/3/035008

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use.

引用

页数：13

共 139 条

[1] Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods [J].