README

This package contains Matlab and R implementations of the algorithms proposed in "Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning", which is appearing in Bioinformatics.

demo_classification.m file shows how to use the classification algorithm in Matlab.
demo_classification.R file shows how to use the classification algorithm in R.
demo_regression.m file shows how to use the regression algorithm in Matlab.
demo_regression.R file shows how to use the regression algorithm in R.

KBMTL methods
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* kbmtl_semisupervised_classification_variational_train.m => training procedure for classification in Matlab
* kbmtl_semisupervised_classification_variational_test.m => test procedure for classification in Matlab
* kbmtl_semisupervised_classification_variational_train.R => training procedure for classification in R
* kbmtl_semisupervised_classification_variational_test.R => test procedure for classification in R
* kbmtl_semisupervised_regression_variational_train.m => training procedure for regression in Matlab
* kbmtl_semisupervised_regression_variational_test.m => test procedure for regression in Matlab
* kbmtl_semisupervised_regression_variational_train.R => training procedure for regression in R
* kbmtl_semisupervised_regression_variational_test.R => test procedure for regression in R

If you use any of the algorithms implemented in this package, please cite the following paper:

Mehmet Gonen and Adam A. Margolin. Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning. Bioinformatics, 30(17):i556-i563, 2014.