IDO: Inferring describable disease-gene relationships using opinion sentences
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Yoon, Y. | - |
dc.contributor.author | Park, S. | - |
dc.date.available | 2020-02-28T03:43:54Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/8822 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.relation.isPartOf | Proceedings of the ACM Symposium on Applied Computing | - |
dc.subject | Algorithms | - |
dc.subject | Data mining | - |
dc.subject | Genes | - |
dc.subject | Networks (circuits) | - |
dc.subject | Analysis | - |
dc.subject | Biological entities | - |
dc.subject | Prostate cancers | - |
dc.subject | Relationship | - |
dc.subject | Specific information | - |
dc.subject | Specific knowledge | - |
dc.subject | Term co-occurrence | - |
dc.subject | Text mining | - |
dc.subject | Diseases | - |
dc.title | IDO: Inferring describable disease-gene relationships using opinion sentences | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1145/2851613.2851616 | - |
dc.identifier.bibliographicCitation | Proceedings of the ACM Symposium on Applied Computing, v.04-08-April-2016, pp.15 - 22 | - |
dc.identifier.scopusid | 2-s2.0-84975883921 | - |
dc.citation.endPage | 22 | - |
dc.citation.startPage | 15 | - |
dc.citation.title | Proceedings of the ACM Symposium on Applied Computing | - |
dc.citation.volume | 04-08-April-2016 | - |
dc.contributor.affiliatedAuthor | Yoon, Y. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Analysis | - |
dc.subject.keywordAuthor | Disease | - |
dc.subject.keywordAuthor | Gene | - |
dc.subject.keywordAuthor | Network | - |
dc.subject.keywordAuthor | Relationship | - |
dc.subject.keywordAuthor | Text-mining | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Data mining | - |
dc.subject.keywordPlus | Genes | - |
dc.subject.keywordPlus | Networks (circuits) | - |
dc.subject.keywordPlus | Analysis | - |
dc.subject.keywordPlus | Biological entities | - |
dc.subject.keywordPlus | Prostate cancers | - |
dc.subject.keywordPlus | Relationship | - |
dc.subject.keywordPlus | Specific information | - |
dc.subject.keywordPlus | Specific knowledge | - |
dc.subject.keywordPlus | Term co-occurrence | - |
dc.subject.keywordPlus | Text mining | - |
dc.subject.keywordPlus | Diseases | - |
dc.description.journalRegisteredClass | scopus | - |
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