Cancer classification and pathway discovery using non-negative matrix factorization.

Journal: Journal of biomedical informatics

Published Date: Aug 1, 2019

Abstract

OBJECTIVES: Extracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.

Authors

Zexian Zeng

Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Andy H Vo

Committee on Developmental Biology and Regenerative Medicine, The University of Chicago, Chicago, IL, USA.
Chengsheng Mao

Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
Susan E Clare

Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Seema A Khan

Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Yuan Luo

Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.

Keywords

Algorithms Cell Line, Tumor Databases, Genetic Diagnosis, Computer-Assisted Exome Gene Expression Regulation, Neoplastic Humans Mutation Neoplasms Pilot Projects Regression Analysis Reproducibility of Results Sequence Analysis, DNA Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (31271844)

Cancer classification and pathway discovery using non-negative matrix factorization.

Abstract

Authors

Keywords

External Resources

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