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Ainscough, 2017 - Google Patents

Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics

Ainscough, 2017

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Document ID
16491897375442979974
Author
Ainscough B
Publication year

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For cancer genomics to fully expand its utility from research discovery to clinical adoption, somatic variant detection pipelines must be optimized and standardized to ensure identification of clinically relevant mutations and to reduce laborious and error-prone post …
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    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
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    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
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