title: Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning.
abstract: BACKGROUND
  Human immunodeficiency virus (HIV) and cancer require personalized therapies owing to their inherent heterogeneous nature. For both diseases, large-scale pharmacogenomic screens of molecularly characterized samples have been generated with the hope of identifying genetic predictors of drug susceptibility. Thus, computational algorithms capable of inferring robust predictors of drug responses from genomic information are of great practical importance. Most of the existing computational studies that consider drug susceptibility prediction against a panel of drugs formulate a separate learning problem for each drug, which cannot make use of commonalities between subsets of drugs.
  
  
  RESULTS
  In this study, we propose to solve the problem of drug susceptibility prediction against a panel of drugs in a multitask learning framework by formulating a novel Bayesian algorithm that combines kernel-based non-linear dimensionality reduction and binary classification (or regression). The main novelty of our method is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps us to eliminate off-target effects and drug-specific experimental noise. Another novelty of our method is the ability of handling missing phenotype values owing to experimental conditions and quality control reasons. We demonstrate the performance of our algorithm via cross-validation experiments on two benchmark drug susceptibility datasets of HIV and cancer. Our method obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.
  
  
  METHODS
  Our Matlab implementations for binary classification and regression are available at https://github.com/mehmetgonen/kbmtl.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Sage Bionetworks, Seattle, WA 98109, USA.
authors.forename: Mehmet
authors.initials: M
authors.lastname: Gönen
authors.affiliation: Sage Bionetworks, Seattle, WA 98109, USA.
authors.forename: Adam A
authors.initials: AA
authors.lastname: Margolin
mesh_terms: D000465:Algorithms; D019380:Anti-HIV Agents / Q000494:pharmacology*; D000970:Antineoplastic Agents / Q000494:pharmacology; D001499:Bayes Theorem; D019008:Drug Resistance, Neoplasm; D024882:Drug Resistance, Viral; D015497:HIV-1 / Q000187:drug effects; D006801:Humans; D012984:Software
chemical_list: D019380:Anti-HIV Agents; D000970:Antineoplastic Agents