TREC GENOMICS Track Overview

By William R. Hersh, Ravi Teja Bhupatiraju. 2003

149 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Computational Biology & Bioinformatics

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “e fought in the context of scientiﬁc articles in the life sciences [Salton, 1972] (see Section 1.2.1). TREC tracks have been organized in this domain as well: the Genomics Track, which ran from 2003 [Hersh and Bhupatiraju, 2003] to 2007 [Hersh et al., 2007], represented information needs closer to t”
Evaluating sentence-level relevance feedback for high-recall information retrieval (2018, methodology): “Prior to CLEF 2017, the systematic review literature described primarily text categorization efforts similar to those employed by the TREC Legal Track, in which the vailable data were partitioned into training and test sets, and effectiveness evaluated with respect to classific tion or ranking of th”
Literature mining for the biologist: from information retrieval to biological discovery (2006, background): “However, blind assessments ( Box 2 ) have shown that most of the lessons that have been learned from IR in other research fields carry over to the biomedical science”
Frontiers of biomedical text mining: current progress (2007, methodology): “Thirty-one groups participated in the Genomics Track, and obtained the following mean average precision scores: 0.0198–0.5439 (median: 0.3083) for document retrieval, 0.0007–0.1486 (median: 0.0345) for passage retrieval and 0.011–0.4411 (median 0.1581) for aspect retrieval.”
TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19 (2020, background): “promoting scientific discovery [3,4], to providing clinical decision support [5,6], to addressing the health needs of consumers and combating misinformation [7].”
Biomedical language processing: what's beyond PubMed? (2006, background): “Perhaps the most influential of these evaluations has been the TREC Genomics Track ( Hersh and Bhupatiraju, 2003; Hersh et al., 2004, 2005).”
Text-mining and information-retrieval services for molecular biology (2005, methodology): “The improvement also reflects the effect of community-wide assessments such as the BioCreative contest [19] and the KDD challenge cup [78], which enable evaluation of the efficiency of different methodologies, and the genomics track of the Text Retrieval Conference (TREC) workshops [79,80], a forum ”