IDO: Inferring describable disease-gene relationships using opinion sentences

Jeongwoo Kim, Youngmi Yoon, Sanghyun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Text mining is widely used to infer relationships between biological entities. Most text-mining algorithms utilize a cooccurrence-based approach. The term co-occurrence denotes a relationship between two interesting entities if they appear in the same sentence. Using these approaches current studies have extracted relationships between biological entities such as disease-gene relationships. However, these approaches cannot provide specific information for inferred relationships such as the role of the gene in the disease. To overcome this limitation, we propose a novel approach for inferring disease-gene relationship that provides specific knowledge of the inferred relationships. To implement this method, we first built terms based on text analysis to extract opinion sentences that include disease-gene relationships. We then extracted these opinion sentences and inferred disease-gene relationships by using disease-related and gene-related terms in the opinion sentences. Using these extracted relationships and terms, we inferred disease-related genes and constructed a disease-specific gene network. To validate our approach, we investigated the top k (k = 20) inferred genes for prostate cancer and analyzed the constructed gene network using three network analysis measures. Our approach found more disease-gene relationships than comparable method, and inferred describable disease-gene relationships.

Original languageEnglish
Title of host publication2016 Symposium on Applied Computing, SAC 2016
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450337397
Publication statusPublished - 2016 Apr 4
Event31st Annual ACM Symposium on Applied Computing, SAC 2016 - Pisa, Italy
Duration: 2016 Apr 42016 Apr 8

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other31st Annual ACM Symposium on Applied Computing, SAC 2016

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2015R1A2A1A05001845).

Publisher Copyright:
© 2016 ACM.

All Science Journal Classification (ASJC) codes

  • Software


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