Associative Naïve Bayes classifier: Automated linking of gene ontology to medline documents

• Published in: Pattern recognition Vol. 42; no. 9; pp. 1777 - 1785
• Main Authors: Kim, Hyunki, Chen, Su-Shing
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2009; Elsevier
• Subjects: Applied sciences; Data mining; Document classification; Exact sciences and technology; Gene ontology; Information theory; Information, signal and communications theory; Knowledge discovery; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Telecommunications and information theory; Gene ontology; Knowledge discovery; Data mining; Document classification; Performance evaluation; Text mining; Automatic classification; Knowledge representation; Data processing; Support vector machine; Signal classification; Hierarchized structure
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2009.01.020

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.