This repository contains R implementation of the algorithms proposed in "SpMKL: Sparse multiple kernel learning via optimal kernel selection with application to cancer biology" (manuscript under review).

• 1_run_SpMKL_Step1.R : shows how to solve the first step SpMKL problem based on the TCGA cohorts and Hallmark pathways to produce significance values for the second step.
• 2_produce_kernel_significance_values_from_step1.R : produces the significance values for each cohort by combining the results obtained in step 1.
• 3_run_SpMKL_Step2.R : shows how to perform the final step of SpMKL on the TCGA cohorts using the significance matrix produced in the previous step.

SpMKL methods

• classification_helper.R => helper functions
• solve_classification_models_cplex.R => support vector machine classification solver, and the cutting plane model using CPLEX optimization software
• solve_classification_models_mosek.R => support vector machine classification solver, and the cutting plane model using Mosek optimization software
• spmkl_train.R => training procedures used in different steps and phases of SpMKL
• spmkl_test.R => test procedure for SpMKL