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

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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

Franceschini, A, Lin, J, von Mering, C & Jensen, LJ 2016, 'SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles', Bioinformatics, vol. 32, no. 7, pp. 1085-7. https://doi.org/10.1093/bioinformatics/btv696

APA

Franceschini, A., Lin, J., von Mering, C., & Jensen, L. J. (2016). SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Bioinformatics, 32(7), 1085-7. https://doi.org/10.1093/bioinformatics/btv696

Vancouver

Franceschini A, Lin J, von Mering C, Jensen LJ. SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Bioinformatics. 2016;32(7):1085-7. https://doi.org/10.1093/bioinformatics/btv696

Author

Franceschini, Andrea ; Lin, Jianyi ; von Mering, Christian ; Jensen, Lars Juhl. / SVD-phy : improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. In: Bioinformatics. 2016 ; Vol. 32, No. 7. pp. 1085-7.

Bibtex

@article{c8fe7fa53025481bada38285fa08f496,
title = "SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles",
abstract = "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.",
author = "Andrea Franceschini and Jianyi Lin and {von Mering}, Christian and Jensen, {Lars Juhl}",
note = "{\textcopyright} The Author(s) 2015. Published by Oxford University Press.",
year = "2016",
doi = "10.1093/bioinformatics/btv696",
language = "English",
volume = "32",
pages = "1085--7",
journal = "Computer Applications in the Biosciences",
issn = "1471-2105",
publisher = "Oxford University Press",
number = "7",

}

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