Article

CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancer

2020-10-08

Abstract excerpt

<h4>ABSTRACT</h4> Several knowledgebases, such as CIViC and OncoKB, have been manually curated to support clinical interpretations of a limited number of “hotspot” somatic mutations in cancer, yet discrepancies or even conflicting interpretations have been observed among these knowledgebases. Additionally, while these knowledgebases have been extremely useful, they typically cannot interpret novel mutations, whic...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 1ee9effc-0379-507b-9bb9-ee12afd84dea
DOI: 10.1101/2020.10.06.323162

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancerDOI 10.1101/2020.10.06.323162Read

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer.Semantic proximity Read

Azurify integrates cancer genomics with machine learning to classify the clinical significance of somatic variantsSemantic proximity Read

Variant Interpretation for Cancer (VIC): a computational tool for assessing clinical impacts of somatic variantsSemantic proximity Read

Clinically Actionable Cancer Somatic Variants (CACSV): A tumor interpreted dataset for analytical workflowSemantic proximity Read

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtypingSemantic proximity Read

Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.Semantic proximity Read

CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase.Semantic proximity Read

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?Semantic proximity Read

Predictive Modeling of Novel Somatic Mutation Impacts on Cancer Prognosis: A Machine Learning Approach Using the COSMIC DatabaseSemantic proximity Read

MARiO: predicting cancer variant pathogenicity by integrating <i>in silico</i> evaluation and patient-level mutational contextsSemantic proximity Read

Are the Next-Generation Pathogenicity Predictors Applicable to Cancer?Semantic proximity Read

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.Semantic proximity Read