RMTL：一个用于多任务学习的 R 库。
RMTL: an R library for multi-task learning.
机构信息
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.
出版信息
Bioinformatics. 2019 May 15;35(10):1797-1798. doi: 10.1093/bioinformatics/bty831.
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.
动机
多任务学习（MTL）是一种机器学习技术，用于同时学习多个相关的分类或回归任务。尽管它越来越受欢迎，但 MTL 算法目前在广泛使用的软件环境 R 中不可用，这成为其在生物医学研究中应用的瓶颈。
结果
我们开发了一个高效、易用的 R 库用于 MTL（www.r-project.org），其中包含 10 种适用于回归、分类、联合预测器选择、任务聚类、低秩学习和生物网络整合的算法。我们使用模拟数据演示了算法的实用性。
可用性和实现
RMTL 包是一个开源的 R 包，可以在 https://github.com/transbioZI/RMTL 上免费获得。RMTL 也将在 cran.r-project.org 上提供。
补充信息
补充数据可在 Bioinformatics 在线获得。
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