CaDrA: A computational framework for performing candidate driver analyses using binary genomic features

Abstract excerpt

Identifying complementary genetic drivers of a given phenotypic outcome is a challenging task that is important to gaining new biological insight and discovering targets for disease therapy. Existing methods aimed at achieving this task lack analytical flexibility. We developed Candidate Driver Analysis or CaDrA, a framework to identify functionally-relevant subsets of binary genomic features that, together, are a...

Topics

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• Computational Biology
• Exome
• Gene Ontology
• High-Throughput Nucleotide Sequencing
• Machine Learning
• Molecular Sequence Annotation
• Oncogenes
• ROC Curve

Identifiers and source

Literature Corpus work

b5809fce-9fb6-524b-8c27-ae00ab092705

DOI

10.1101/221846