research-article

Hunting for truly relevant articles in bioinformatics literature: a preliminary study

Authors:

Cen Li,

Suk Seo,

Ralph ButlerAuthors Info & Claims

BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

Pages 481 - 483

https://doi.org/10.1145/1854776.1854864

Published: 02 August 2010 Publication History

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Abstract

For researchers interested in reading articles concerning a specific topic, the current document search techniques, based primarily on keyword matching, are insufficient. They tend to return too many "hits", most of which are not truly relevant. An individualized text filtering system that can select/recommend useful articles would be a tremendous time-saver for researchers, especially in the field of bioinformatics, in which numerous articles are published daily. Machine learning tools such as text classification may be the answer to this need. This paper describes some preliminary work on developing such a text filtering system. Support Vector Machine is used to classify articles from Journal of Bacteriology to determine whether an article addresses issues related to "gene function". Preliminary results, problems, and difficulties encountered are discussed.

References

[1]

Burges, C. J. C. A tutorial on support vector machines for pattern recognition, Knowledge discovery and Data Mining, 2, pp. 1--43, 1998.

Digital Library

Google Scholar

[2]

Caruana, R. and Niculescu-Mizil, A. An empirical comparision of supervised learning algorithms. Proceedings of the International Conference on Machine Learning, 2006.

Digital Library

Google Scholar

[3]

Cesa-Bianchi, N. Gentile, C. and Zaniboni, L. Hierarchical classification: combining Bayes with SVM. Proceedings of the International Conference on Machine Learning, 2006.

Digital Library

Google Scholar

[4]

Joachims, T. Making large scale SVM learning practical. Advances in Kernel Methods -- Support Vector Learning, ed. Scholkopf, B, Burges, C. and Smola, A. MIT Press, Cambridge, USA, 1998.

Digital Library

Google Scholar

[5]

Manning, C. D., Raghavan, P., and Schutze H., Introduction to Information Retrieval, Cambridge University Press, 2008.

Digital Library

Google Scholar

[6]

National Center for Biotechnology Information: http://www.ncbi.nlm.nih.gov/

Google Scholar

[7]

Overbeek, R., Begley, T., Butler, R. M., et.al. The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1,000 Genomes, Nucleic Acids Research, Vol. 33, No. 17, pp. 5691--5702, 2005.

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Li CPitigala SSeo SClincy VHoganson KGarrido JDasigi V(2011)Identifying training sets for personalized article retrieval systemProceedings of the 49th annual ACM Southeast Conference10.1145/2016039.2016142(350-351)Online publication date: 24-Mar-2011
https://dl.acm.org/doi/10.1145/2016039.2016142

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BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

August 2010

705 pages

ISBN:9781450304382

DOI:10.1145/1854776

General Chairs:
Aidong Zhang
SUNY at Buffalo
,
Mark Borodovsky
Georgia Tech
,
Program Chairs:
Gultekin Ozsoyoglu
Case Western Reserve University
,
Armin Mikler
University of North Texas

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 02 August 2010

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BCB'10: ACM International Conference on Bioinformatics and Computational Biology

August 2 - 4, 2010

New York, Niagara Falls

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Li CPitigala SSeo SClincy VHoganson KGarrido JDasigi V(2011)Identifying training sets for personalized article retrieval systemProceedings of the 49th annual ACM Southeast Conference10.1145/2016039.2016142(350-351)Online publication date: 24-Mar-2011
https://dl.acm.org/doi/10.1145/2016039.2016142

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The High Performance Computing Applications for Bioinformatics Research

An empirical comparison of supervised machine learning techniques in bioinformatics

Seq: a high-performance language for bioinformatics

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