Rule clustering and super-rule generation for transmembrane segments prediction

The explanation of a decision is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. In past research, we have already combined SVM with decision tree to extract rules for understanding transmembrane segments prediction. Ho...

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Bibliographic Details
Main Authors: Jieyue He, Bernard Chen, Hae-Jin Hu, Harrison, R., Tai, P.C., Yisheng Dong, Yi Pan
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Bioinformatics
Biology computing
Clustering algorithms
Computer science
Decision trees
Encoding
Machine learning
Proteins
Support vector machines
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Summary:The explanation of a decision is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. In past research, we have already combined SVM with decision tree to extract rules for understanding transmembrane segments prediction. However, rules we have gotten are as many as about 20,000. This large number of rules makes them difficult for us to interpret their meaning. In this paper, a novel approach of rule clustering (SVM/spl I.bar/DT/spl I.bar/C) for super-rule generation is presented. We use K-means clustering to cluster huge number of rules to generate many new super-rules. The experimental results show that the super-rules produced by SVM/spl I.bar/DT/spl I.bar/C can be analyzed manually by a researcher, and these super-rules are not only new but also achieve very high transmembrane prediction accuracy (exceeding 95%) most of the times.
DOI:10.1109/CSBW.2005.121