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 = { c 1 , c 2 , … , c n } 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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