Work

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data

Year: 2018

Type: article

Source: Nature Genetics

DOI: 10.1038/s41588-018-0257-y

Authors: Benjamin J. Ainscough, Erica K. Barnell, Peter Ronning, Katie M. Campbell, Alex H. Wagner, Todd A. Fehniger, Gavin P. Dunn, Ravindra Uppaluri, Ramaswamy Govindan, Thomas E. Rohan, Malachi Griffith, Elaine R. Mardis, S. Joshua Swamidass, Obi L. Griffith

Institutions: Washington University in St. Louis, Alvin J. Siteman Cancer Center, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Dana-Farber Brigham Cancer Center, Albert Einstein College of Medicine, Nationwide Children's Hospital, Abigail Wexner Research Institute, The Ohio State University

Language: English

Topic: Cancer Genomics and Diagnostics

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Dental and Craniofacial Research, U.S. National Library of Medicine

Awards: G1515700839, G1686905186, G1733257139, G2025076051, G2265240306, G2420751776, G304139194, Genomic and Functional Identification of Chemotherapy Resistance Mechanisms in Small Cell Lung Cancer, G5310623310, G581057067, G5841304809, G6570698260, DEFINING THE REGULATORY, NON-CODING, MUTATIONAL LANDSCAPE OF BREAST CANCER, National Research Service Award-Medical Scientist, G87200534, Genomics Of Acute Myeloid Leukemia.

OpenAlex ID: W2898882839

FWCI: 2.506

Cites: 51

Cited by: 87

Related to: 10