SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
SVD-phy : improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. / Franceschini, Andrea; Lin, Jianyi; von Mering, Christian; Jensen, Lars Juhl.
In: Bioinformatics, Vol. 32, No. 7, 2016, p. 1085-7.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - SVD-phy
T2 - improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles
AU - Franceschini, Andrea
AU - Lin, Jianyi
AU - von Mering, Christian
AU - Jensen, Lars Juhl
N1 - © The Author(s) 2015. Published by Oxford University Press.
PY - 2016
Y1 - 2016
N2 - 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.AVAILABILITY AND IMPLEMENTATION: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy CONTACT: lars.juhl.jensen@cpr.ku.dk.
AB - 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.AVAILABILITY AND IMPLEMENTATION: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy CONTACT: lars.juhl.jensen@cpr.ku.dk.
U2 - 10.1093/bioinformatics/btv696
DO - 10.1093/bioinformatics/btv696
M3 - Journal article
C2 - 26614125
VL - 32
SP - 1085
EP - 1087
JO - Computer Applications in the Biosciences
JF - Computer Applications in the Biosciences
SN - 1471-2105
IS - 7
ER -
ID: 152245231