Scientific Reports · 2017 · 72 citations · 31 references
MedicineBioinformaticsOncologyCancer GeneticsGenomicsMolecular OncologyCancer GenomicsComputational OncologyMolecular Classification Of TumorsMolecular Tumor ProfilingOncogene MutationsMissense MutationsOncogenicity ScoringPrioritization
The complicated, evolving landscape of cancer mutations poses a formidable challenge to identify cancer genes among the large lists of mutations typically generated in NGS experiments. The ability to prioritize these variants is therefore of paramount importance. To address this issue we developed OncoScore, a text-mining tool that ranks genes according to their association with cancer, based on available biomedical literature. Receiver operating characteristic curve and the area under the curve (AUC) metrics on manually curated datasets confirmed the excellent discriminating capability of OncoScore (OncoScore cut-off threshold = 21.09; AUC = 90.3%, 95% CI: 88.1-92.5%), indicating that OncoScore provides useful results in cases where an efficient prioritization of cancer-associated genes is needed.
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Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li, Richard Durbin · Bioinformatics · 2009 · 62.8K citations · Full text
Index for rating diagnostic tests
W. J. Youden · Cancer · 1950 · 11.5K citations · Full text
Education, Educational Assessment, Educational Measurement +11