Associative Naïve Bayes classifier: Automated linking of gene ontology to medline documents |
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Authors: | Hyunki Kim [Author Vitae] [Author Vitae] |
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Affiliation: | a Electronics and Telecommunications Research Institute, Daejeon 305-700, Republic of Korea b CAS-MPG Partner Institute of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yan Road, Shanghai 200031, China |
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Abstract: | We demonstrate a text-mining method, called associative Naïve Bayes (ANB) classifier, for automated linking of MEDLINE documents to gene ontology (GO). The approach of this paper is a nontrivial extension of document classification methodology from a fixed set of classes C={c1,c2,…,cn} to a knowledge hierarchy like GO. Due to the complexity of GO, we use a knowledge representation structure. With that structure, we develop the text mining classifier, called ANB classifier, which automatically links Medline documents to GO. To check the performance, we compare our datasets under several well-known classifiers: NB classifier, large Bayes classifier, support vector machine and ANB classifier. Our results, described in the following, indicate its practical usefulness. |
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Keywords: | Data mining Knowledge discovery Gene ontology Document classification |
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