Report for: A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Title	A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status
Published in	PLOS ONE, December 2013
DOI	10.1371/journal.pone.0082144
Pubmed ID	24312637
Authors	Meysam Bastani, Larissa Vos, Nasimeh Asgarian, Jean Deschenes, Kathryn Graham, John Mackey, Russell Greiner
Abstract	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 reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results.

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Country	Count	As %
Unknown	2	100%

Type	Count	As %
Scientists	1	50%
Members of the public	1	50%

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Readers by professional status	Count	As %
Researcher	10	16%
Other	6	10%
Student > Ph. D. Student	6	10%
Student > Bachelor	5	8%
Student > Master	4	7%
Other	15	25%
Unknown	15	25%

Readers by discipline	Count	As %
Medicine and Dentistry	15	25%
Computer Science	8	13%
Agricultural and Biological Sciences	6	10%
Engineering	6	10%
Biochemistry, Genetics and Molecular Biology	5	8%
Other	5	8%
Unknown	16	26%

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