A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer

Abstract Background Cell-free DNA’s (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor vari...

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Bibliographic Details
Main Authors: Clinton L. Cario, Emmalyn Chen, Lancelote Leong, Nima C. Emami, Karen Lopez, Imelda Tenggara, Jeffry P. Simko, Terence W. Friedlander, Patricia S. Li, Pamela L. Paris, Peter R. Carroll, John S. Witte
Format: Article
Language:English
Published: BMC 2020-08-01
Series:BMC Cancer
Subjects:
Cell-free DNA
Prostate cancer
Machine learning
Panel design
Tumor variant detection
Online Access:http://link.springer.com/article/10.1186/s12885-020-07318-x

Internet

http://link.springer.com/article/10.1186/s12885-020-07318-x

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