RMTL: an R library for multi-task learning

Author:

Cao Han1,Zhou Jiayu2,Schwarz Emanuel1

Affiliation:

1. Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

2. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

Abstract

Abstract Motivation Multi-task learning (MTL) is a machine learning technique for simultaneous learning of multiple related classification or regression tasks. Despite its increasing popularity, MTL algorithms are currently not available in the widely used software environment R, creating a bottleneck for their application in biomedical research. Results We developed an efficient, easy-to-use R library for MTL (www.r-project.org) comprising 10 algorithms applicable for regression, classification, joint predictor selection, task clustering, low-rank learning and incorporation of biological networks. We demonstrate the utility of the algorithms using simulated data. Availability and implementation The RMTL package is an open source R package and is freely available at https://github.com/transbioZI/RMTL. RMTL will also be available on cran.r-project.org. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Deutsche Forschungsgemeinschaft

DFG

SCHW

National Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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