Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

Journal of Proteome Research

Volumn 17, Issue 1, 2018, Pages 337-347

  Alakwaa, Fadhl M ^a   Chaudhary, Kumardeep ^a   Garmire, Lana X ^a,b  

^a UNIVERSITY OF HAWAII CANCER CENTER   (United States)

^b UNIVERSITY OF HAWAII   (United States)

Author keywords

bioinformatics; breast cancer; deep learning; estrogen receptor; metabolomics

Indexed keywords

ESTROGEN RECEPTOR;

ABSORPTION; ARTICLE; BREAST CANCER; COMPARATIVE STUDY; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; GAS CHROMATOGRAPHY; GENE EXPRESSION; GENERALIZED BOOSTED MODELS; HUMAN; HUMAN CELL; HUMAN TISSUE; K NEAREST NEIGHBOR; LINEAR DISCRIMINANT ANALYSIS; MACHINE LEARNING; MCF-10A CELL LINE; MCF-7 CELL LINE; MDA-MB-231 CELL LINE; METABOLOMICS; PREDICTION ANALYSIS FOR MICROARRAYS; PRIORITY JOURNAL; RANDOM FOREST; RECURSIVE PARTITIONING AND REGRESSION TREES; SUPPORT VECTOR MACHINE; AREA UNDER THE CURVE; BREAST TUMOR; CLASSIFICATION; FEMALE; PROCEDURES; STANDARDS;

AREA UNDER CURVE; BREAST NEOPLASMS; FEMALE; HUMANS; MACHINE LEARNING; METABOLOMICS; RECEPTORS, ESTROGEN;

EID: 85040193110     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/acs.jproteome.7b00595     Document Type: Article

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