title: Enhancing the accuracy of knowledge discovery: a supervised learning method.
abstract: BACKGROUND
  The amount of biomedical literature available is growing at an explosive speed, but a large amount of useful information remains undiscovered in it. Researchers can make informed biomedical hypotheses through mining this literature. Unfortunately, popular mining methods based on co-occurrence produce too many target concepts, leading to the declining relevance ranking of the potential target concepts.
  
  
  METHODS
  This paper presents a new method for selecting linking concepts which exploits statistical and textual features to represent each linking concept, and then classifies them as relevant or irrelevant to the starting concepts. Relevant linking concepts are then used to discover target concepts.
  
  
  RESULTS
  Through an evaluation it is observed textual features improve the results obtained with only statistical features. We successfully replicate Swanson's two classic discoveries and find the rankings of potentially relevant target concepts are relatively high.
  
  
  CONCLUSIONS
  The number of target concepts is greatly reduced and potentially relevant target concepts gain higher ranking by adopting only relevant linking concepts. Thus, the proposed method has the potential to help biomedical experts find the most useful and valuable target concepts effectively.
authors.forename: Liangxi
authors.initials: L
authors.lastname: Cheng
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lin
authors.forename: Feng
authors.initials: F
authors.lastname: Zhou
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Yang
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
mesh_terms: D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D016239:MEDLINE; D060388:Support Vector Machine