Information Retrieval for Precision Oncology
| dc.contributor.author | Seva, Jurica | |
| dc.contributor.author | Goetze, Julian | |
| dc.contributor.author | Lamping, Mario | |
| dc.contributor.author | Rieke, Damian Tobias | |
| dc.contributor.author | Schaefer, Reinhold | |
| dc.contributor.author | Leser, Ulf | |
| dc.contributor.editor | Grust, Torsten | |
| dc.contributor.editor | Naumann, Felix | |
| dc.contributor.editor | Böhm, Alexander | |
| dc.contributor.editor | Lehner, Wolfgang | |
| dc.contributor.editor | Härder, Theo | |
| dc.contributor.editor | Rahm, Erhard | |
| dc.contributor.editor | Heuer, Andreas | |
| dc.contributor.editor | Klettke, Meike | |
| dc.contributor.editor | Meyer, Holger | |
| dc.date.accessioned | 2019-04-11T07:21:31Z | |
| dc.date.available | 2019-04-11T07:21:31Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient’s genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of search results in the common search engines focuses on basic science and is clinically irrelevant. We developed the Variant-Information Search Tool, a search engine designed for the targeted search of clinically relevant publications given a mutation profile. VIST indexes all PubMed abstracts, applies advanced text mining to identify mentions of genes and variants and uses machine-learning based scoring to judge the relevancy of documents. Its functionality is available through a fast and intuitive web interface. We also performed a comparative evaluation, showing that VIST’s ranking is superior to that of PubMed or vector space models. | en |
| dc.identifier.doi | 10.18420/btw2019-39 | |
| dc.identifier.isbn | 978-3-88579-683-1 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/21725 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | BTW 2019 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) – Proceedings, Volume P-289 | |
| dc.title | Information Retrieval for Precision Oncology | en |
| gi.citation.endPage | 536 | |
| gi.citation.startPage | 533 | |
| gi.conference.date | 4.-8. März 2019 | |
| gi.conference.location | Rostock | |
| gi.conference.sessiontitle | Demonstrationen |
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