Prediction of EST functional relationships via literature mining with user-specified parameters

Hei-Chia Wang, Tian Hsiang Huang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

Original languageEnglish
Article number4694115
Pages (from-to)969-977
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume56
Issue number4
DOIs
Publication statusPublished - 2009 Apr 1

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All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

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Prediction of EST functional relationships via literature mining with user-specified parameters. / Wang, Hei-Chia; Huang, Tian Hsiang.

In: IEEE Transactions on Biomedical Engineering, Vol. 56, No. 4, 4694115, 01.04.2009, p. 969-977.

Research output: Contribution to journalArticle

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