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Bioinformatics Advance Access published online on July 12, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl350
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© 2006 The Author(s)
Received March 2, 2006
Revised June 4, 2006
Accepted June 23, 2006

Article

Substring selection for biomedical document classification

Bo Han 1, Zoran Obradovic 1, Zhang-Zhi Hu 2, Cathy H. Wu 2, and Slobodan Vucetic 1 *

1 Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA
2 Department of Biochemistry and Molecular Biology, Georgetown University Medical Center, Washington, DC 20057, USA

* To whom correspondence should be addressed.
Slobodan Vucetic, E-mail: vucetic{at}ist.temple.edu


  Abstract

Motivation: Attribute selection is a critical step in development of document classification systems. As a standard practice, words are stemmed and the most informative ones are used as attributes in classification. Due to high complexity of biomedical terminology, general-purpose stemming algorithms are often conservative and could also remove informative stems. This can lead to accuracy reduction, especially when the number of labeled documents is small. To address this issue, we propose an algorithm that omits stemming and, instead, uses the most discriminative substrings as attributes.

Results: The approach was tested on five annotated sets of abstracts from iProLINK that report on the experimental evidence about five types of protein post-translational modifications. The experiments showed that Naive Bayes and Support Vector Machine classifiers perform consistently better (with Area Under the ROC Curve (AUC) accuracy in range 0.92-0.97) when using the proposed attribute selection than when using attributes obtained by the Porter stemmer algorithm (AUC in 0.86-0.93 range). The proposed approach is particularly useful when labeled datasets are small.

Availability: The supplementary data is available from www.ist.temple.edu/PIRsupplement.

Associate Editor: Thomas Lengauer
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[Abstract] [Full Text] [PDF]


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