title: Rule extraction in gene-disease relationship discovery.
abstract: BACKGROUND
  Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy.
  
  
  METHODS
  Our study uses the automatic rule-learning approach to gene-disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated.
  
  
  RESULTS
  We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%.
  
  
  CONCLUSIONS
  We employ the rule-learning approach to extract gene-disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan. emilyhou@csie.ntnu.edu.tw
authors.forename: Wen-Juan
authors.initials: WJ
authors.lastname: Hou
authors.forename: Hsiao-Yuan
authors.initials: HY
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D006801:Humans; D016239:MEDLINE