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Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts

2017-09-12

Abstract excerpt

<h4>Background:</h4> Approaches that capitalize on the benefits of multi-omic data integration in invasive breast carcinoma to define prognostic biomarkers for precision medicine have been slow to emerge. In this work, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors as part of a single analysis to create prognostic risk scores...

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Literature Corpus work
3bb2ddf8-6971-5d94-81fe-0a6de54943aa
DOI
10.1101/187526
Open publication

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Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple CohortsDOI 10.1101/187526
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