title: A literature-driven method to calculate similarities among diseases.
abstract: BACKGROUND
  "Our lives are connected by a thousand invisible threads and along these sympathetic fibers, our actions run as causes and return to us as results". It is Herman Melville's famous quote describing connections among human lives. To paraphrase the Melville's quote, diseases are connected by many functional threads and along these sympathetic fibers, diseases run as causes and return as results. The Melville's quote explains the reason for researching disease-disease similarity and disease network. Measuring similarities between diseases and constructing disease network can play an important role in disease function research and in disease treatment. To estimate disease-disease similarities, we proposed a novel literature-based method.
  
  
  RESULTS
  The proposed method extracted disease-gene relations and disease-drug relations from literature and used the frequencies of occurrence of the relations as features to calculate similarities among diseases. We also constructed disease network with top-ranking disease pairs from our method. The proposed method discovered a larger number of answer disease pairs than other comparable methods and showed the lowest p-value.
  
  
  CONCLUSIONS
  We presume that our method showed good results because of using literature data, using all possible gene symbols and drug names for features of a disease, and determining feature values of diseases with the frequencies of co-occurrence of two entities. The disease-disease similarities from the proposed method can be used in computational biology researches which use similarities among diseases.
authors.affiliation: Department of Computer Science, Yonsei University, South Korea. Electronic address: chriskim@cs.yonsei.ac.kr.
authors.forename: Hyunjin
authors.initials: H
authors.lastname: Kim
authors.affiliation: Department of Computer Engineering, Gachon University, South Korea. Electronic address: ymyoon@gachon.ac.kr.
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Yoon
authors.affiliation: Department of Integrative Biology and Physiology, University of California, Los Angeles, USA. Electronic address: jgahn@ucla.edu.
authors.forename: Jaegyoon
authors.initials: J
authors.lastname: Ahn
authors.affiliation: Department of Computer Science, Yonsei University, South Korea. Electronic address: sanghyun@cs.yonsei.ac.kr.
authors.forename: Sanghyun
authors.initials: S
authors.lastname: Park
mesh_terms: D000465:Algorithms; D064229:Biological Ontologies; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D004194:Disease / Q000145:classification* / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D015201:Meta-Analysis as Topic; D010506:Periodicals as Topic / Q000706:statistics & numerical data*; D063189:Symptom Assessment / Q000145:classification*
keywords: Biomedical text mining; Disease network; Disease–disease similarity