title: MULTITASK FEATURE SELECTION WITH TASK DESCRIPTORS.
abstract: Machine learning applications in precision medicine are severely limited by the scarcity of data to learn from. Indeed, training data often contains many more features than samples. To alleviate the resulting statistical issues, the multitask learning framework proposes to learn different but related tasks jointly, rather than independently, by sharing information between these tasks. Within this framework, the joint regularization of model parameters results in models with few non-zero coefficients and that share similar sparsity patterns. We propose a new regularized multitask approach that incorporates task descriptors, hence modulating the amount of information shared between tasks according to their similarity. We show on simulated data that this method outperforms other multitask feature selection approaches, particularly in the case of scarce data. In addition, we demonstrate on peptide MHC-I binding data the ability of the proposed approach to make predictions for new tasks for which no training data is available.
authors.affiliation: MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, 35 rue St Honoré 77300 Fontainebleau, France2Institut Curie, 75248 Paris Cedex 05, France3INSERM U900, 75248 Paris Cedex 05, France, victor.bellon@mines-paristech.fr.
authors.forename: Víctor
authors.initials: V
authors.lastname: Bellón
authors.forename: Véronique
authors.initials: V
authors.lastname: Stoven
authors.forename: Chloé-Agathe
authors.initials: CA
authors.lastname: Azencott
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015395:Histocompatibility Antigens Class I / Q000378:metabolism; D006801:Humans; D016018:Least-Squares Analysis; D000069550:Machine Learning / Q000706:statistics & numerical data; D015233:Models, Statistical; D010455:Peptides / Q000378:metabolism; D057285:Precision Medicine / Q000706:statistics & numerical data; D011485:Protein Binding; D012372:ROC Curve
chemical_list: D015395:Histocompatibility Antigens Class I; D010455:Peptides