RMTL: an R library for multi-task learning

Han Cao; Jiayu Zhou; Emanuel Schwarz

doi:10.1093/bioinformatics/bty831

RMTL: an R library for multi-task learning

Bioinformatics. 2019 May 15;35(10):1797-1798. doi: 10.1093/bioinformatics/bty831.

Authors

Han Cao¹, Jiayu Zhou², Emanuel Schwarz¹

Affiliations

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.

PMID: 30256897
DOI: 10.1093/bioinformatics/bty831

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Databases, Genetic
Machine Learning
Software*