IDO: Inferring describable disease-gene relationships using opinion sentences
- Authors
- Kim, J.; Yoon, Y.; Park, S.
- Issue Date
- 2016
- Publisher
- Association for Computing Machinery
- Keywords
- Analysis; Disease; Gene; Network; Relationship; Text-mining
- Citation
- Proceedings of the ACM Symposium on Applied Computing, v.04-08-April-2016, pp.15 - 22
- Journal Title
- Proceedings of the ACM Symposium on Applied Computing
- Volume
- 04-08-April-2016
- Start Page
- 15
- End Page
- 22
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/8822
- DOI
- 10.1145/2851613.2851616
- ISSN
- 0000-0000
- Abstract
- 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. © 2016 ACM.
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