SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles

被引：85

作者：

Franceschini, Andrea ^{[1
,2
]}

Lin, Jianyi ^{[3
]}

von Mering, Christian ^{[1
,2
]}

Jensen, Lars Juhl ^{[4
]}

机构：

[1] Univ Zurich, Inst Mol Life Sci, Winterthurerstr 190, CH-8057 Zurich, Switzerland

[2] Swiss Inst Bioinformat, Batiment Genopode, CH-1015 Lausanne, Switzerland

[3] Univ Milan, Dept Comp Sci, Via Comelico 39, I-20135 Milan, Italy

[4] Univ Copenhagen, Ctr Prot Res, Novo Nordisk Fdn, DK-2200 Copenhagen N, Denmark

来源：

BIOINFORMATICS | 2016年 / 32卷 / 07期

关键词：

IDENTIFICATION; DISCOVERY; PATHWAYS; SYSTEMS;

D O I：

10.1093/bioinformatics/btv696

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods.

引用

页码：1085 / 1087

页数：3

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