BMTMKL: Bayesian Multitask Multiple Kernel Learning [Download]

This software package implements Bayesian multitask multiple kernel learning with a fully Bayesian treatment and with an ability to work with multiple data sources expressed as different kernels and to predict multiple outputs formulated as different tasks. Bayesian multitask multiple kernel learning combines nonlinear regression, multiview learning, multitask learning, and Bayesian inference.

If you use the algorithm implemented in the package, please cite the following paper:

• James C Costello, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad ud din, Petteri Hintsanen, Suleiman A Khan, John-Patrick Mpindi, Olli Kallioniemi, Antti Honkela, Tero Aittokallio, Krister Wennerberg, NCI DREAM Community, James J Collins, Dan Gallahan, Dinah Singer, Julio Saez-Rodriguez, Samuel Kaski, Joe W Gray, and Gustavo Stolovitzky. A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, to appear. [manuscript]

This is experimental software provided as is; we welcome any comments and corrections but cannot give any guarantees about the code. If you have any comments or bug reports, please direct them to Mehmet Gönen.