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Predicting the effects of gene deletion

Authors:

David S. Vogel,

Randy C. AxelrodAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 4, Issue 2

Pages 101 - 103

https://doi.org/10.1145/772862.772879

Published: 01 December 2002 Publication History

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Abstract

In this paper, we describe techniques that can be used to predict the effects of gene deletion. We will focus mainly on the creation of predictive variables, and then briefly discuss different modeling techniques that have been used successfully on this data.

References

[1]

Saccharomyces Genome Deletion Project web page, the list of essential ORFs link: http://www-sequence.stanford.edu/group/yeast_deletion_project/Essential_ORFs.txt

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[2]

Functional Profiling of the Saccharomyces cerevisiae Genome. Nature 418: 387--391 (2002).

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[3]

Winzeler, E., Shoemaker, D., Astromoff, A. Liang, H., et al. Functional Characterization of the Saccharomyces cerevisiae Genome by Gene Deletion and Parallel Analysis. Science. 285, 901--906. (1999).

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[4]

Shoemaker, D., Lashkari, D.A., Morris, D., Mittmann, M. & Davis, R.W. Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nature Genetics. 14, 450--456 (1996).

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Frunza OInkpen DDemner-Fushman DAnaniadou SCohen KPestian JTsujii JWebber B(2008)Textual information for predicting functional properties of the genesProceedings of the Workshop on Current Trends in Biomedical Natural Language Processing10.5555/1572306.1572334(110-111)Online publication date: 19-Jun-2008
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Published In

ACM SIGKDD Explorations Newsletter Volume 4, Issue 2

December 2002

127 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/772862

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Association for Computing Machinery

New York, NY, United States

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Published: 01 December 2002

Published in SIGKDD Volume 4, Issue 2

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Lulli GMessina EArchetti FLanzeni S(2009)A Mathematical Model for Optimal Functional Disruption of Biochemical NetworksJournal of Mathematical Modelling and Algorithms10.1007/s10852-009-9118-09:1(19-37)Online publication date: 7-Nov-2009
https://doi.org/10.1007/s10852-009-9118-0
Frunza OInkpen DDemner-Fushman DAnaniadou SCohen KPestian JTsujii JWebber B(2008)Textual information for predicting functional properties of the genesProceedings of the Workshop on Current Trends in Biomedical Natural Language Processing10.5555/1572306.1572334(110-111)Online publication date: 19-Jun-2008
https://dl.acm.org/doi/10.5555/1572306.1572334
Zhang LWong SKing ORoth F(2004)Predicting co-complexed protein pairs using genomic and proteomic data integrationBMC Bioinformatics10.1186/1471-2105-5-385:1Online publication date: 16-Apr-2004
https://doi.org/10.1186/1471-2105-5-38
Krogel MScheffer T(2004)Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional GenomicsMachine Language10.1023/B:MACH.0000035472.73496.0c57:1-2(61-81)Online publication date: 1-Oct-2004
https://dl.acm.org/doi/10.1023/B%3AMACH.0000035472.73496.0c

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Support vector machine method on predicting resistance gene against Xanthomonas oryzae pv. oryzae in rice

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