Figure 1. Overview of median-supplement methods

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Figure 1. Overview of median-supplement methods Figure 1. Overview of median-supplement methods. The scalar multiplication of the median expression of each gene and a corresponding column vector of a random matrix are aggregated to the gene expressions to form a median-supplement data. The random matrix is generated using a Latin Hypercube. A model for inferring receptor status of a new patient is constructed from the median-supplement data. From: Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer Brief Bioinform. Published online October 16, 2017. doi:10.1093/bib/bbx138 Brief Bioinform | © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Figure 2. Accuracy of machine learning methods to identify HER2 status of breast cancer patients. (A) Performance on HER2 receptor data from a group of 162 instances of breast cancer patients. (B) Performance on HER2 receptor data from a group of 806 instances of breast cancer patients. Bars represent the rate of correctly identifying HER2 status of breast cancer patient. SVM is support vector machine, logistic is logistic regression, BN(RHC) is Bayesian network with repeated hill climbing search, BN(SA) is Bayesian network with simulated annealing, NB is Naive Bayes, RT is Random Trees, RF is Random Forest, MNB is median-supplement Naive Bayes and MRF is median-supplement Random Forest. From: Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer Brief Bioinform. Published online October 16, 2017. doi:10.1093/bib/bbx138 Brief Bioinform | © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Figure 3. Accuracy of machine learning methods to identify PR status phenotype of breast cancer patients. (A) Performance on PR data from a group of 162 instances of breast cancer patients. (B) Performance on PR data from a group of 1146 instances of breast cancer patients. Bars represent the rate of correctly identifying PR status phenotype of breast cancer patient. SVM is support vector machine, logistic is logistic regression, BN(RHC) is Bayesian network with repeated hill climbing search, BN(SA) is Bayesian network with simulated annealing (excluded in (B) because of the size of data), NB is Naive Bayes, RT is Random Trees, RF is Random Forest, MNB is median-supplement Naive Bayes and MRF is median-supplement Random Forest. From: Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer Brief Bioinform. Published online October 16, 2017. doi:10.1093/bib/bbx138 Brief Bioinform | © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Figure 4. Accuracy of machine learning methods to identify ER status phenotype of breast cancer patients. (A) Performance on ER data from a group of 162 instances of breast cancer patients. (B) Performance on ER data from a group of 1149 instances of breast cancer patients. Bars represent the rate of correctly identifying ER status of breast cancer patient. SVM is support vector machine, logistic is logistic regression, BN(RHC) is Bayesian network with repeated hill climbing search, BN(SA) is Bayesian network with simulated annealing (excluded in (B) because of the size of data), NB is Naive Bayes, RT is Random Trees, RF is Random Forest, MNB is median-supplement Naive Bayes and MRF is Median-supplement Random Forest. From: Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer Brief Bioinform. Published online October 16, 2017. doi:10.1093/bib/bbx138 Brief Bioinform | © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Figure 5. Effects of different samples of receptor status phenotypes on the performances of median-supplement methods. MNB is median-supplement Naive Bayes and MRF is median-supplement Random Forest. From: Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer Brief Bioinform. Published online October 16, 2017. doi:10.1093/bib/bbx138 Brief Bioinform | © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com