Journal
PATTERN RECOGNITION
Volume 42, Issue 9, Pages 1777-1785Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2009.01.020
Keywords
Data mining; Knowledge discovery; Gene ontology; Document classification
Funding
- Korea Evaluation Institute of Industrial Technology (KEIT) [KI001807] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
We demonstrate a text-mining method, called associative Naive 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. (C) 2009 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available