A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status

Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and repro...

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Veröffentlicht in:	PloS one 2013-12, Vol.8 (12), p.e82144-e82144
Hauptverfasser:	Bastani, Meysam, Vos, Larissa, Asgarian, Nasimeh, Deschenes, Jean, Graham, Kathryn, Mackey, John, Greiner, Russell
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Biomarkers Biostatistics Breast cancer Breast Neoplasms - metabolism Cancer therapies Chemotherapy Classifiers Computational Biology - methods Development and progression DNA microarrays Endocrine therapy Estrogen Estrogen receptors Estrogens Formaldehyde Gene expression Gene Expression Profiling Genetic aspects Humans Laboratories Learning algorithms Machine learning Medical prognosis Oncology Paraffin Physiological aspects Receptors Receptors, Estrogen - metabolism Reproducibility Ribonucleic acid RNA Test procedures Tumors
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