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Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data Fadhl M. Alkawaa, Kumardeep Chaudhary, and Lana X. Garmire J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00595 • Publication Date (Web): 07 Nov 2017 Downloaded from http://pubs.acs.org on November 8, 2017
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Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
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Journal of Proteome Research
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Deep learning accurately predicts estrogen receptor status in breast
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cancer metabolomics data
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Fadhl M Alakwaa1, Kumardeep Chaudhary1, Lana X Garmire1,2*
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Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA.
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Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa,
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Honolulu, HI 96822, USA.
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*To whom the correspondence should be addressed:
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Lana X Garmire, PhD
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Associate Professor
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Email address:
[email protected] 11
Phone: +1 (808) 441-8193
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ACS Paragon Plus Environment
Journal of Proteome Research
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ABSTRACT
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Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases.
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Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine
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learning based classification methods. However, it remains unknown if deep neural network, a class of
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increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a
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cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen
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receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six
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widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM),
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Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction
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Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM). DL framework has the
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highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six
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machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals
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eight commonly enriched significant metabolomics pathways (adjusted P-value
