Bioinformatics and Computational Biology, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455.
Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455.
Gigascience. 2019 May 1;8(5). doi: 10.1093/gigascience/giz042.
The use of machine learning in high-dimensional biological applications, such as the human microbiome, has grown exponentially in recent years, but algorithm developers often lack the domain expertise required for interpretation and curation of the heterogeneous microbiome datasets. We present Microbiome Learning Repo (ML Repo, available at https://knights-lab.github.io/MLRepo/), a public, web-based repository of 33 curated classification and regression tasks from 15 published human microbiome datasets. We highlight the use of ML Repo in several use cases to demonstrate its wide application, and we expect it to be an important resource for algorithm developers.
近年来，机器学习在人类微生物组等高维生物学应用中的使用呈指数级增长，但算法开发人员通常缺乏解释和管理异质微生物组数据集所需的领域专业知识。我们介绍了微生物组学习资源库 (ML Repo，可在 https://knights-lab.github.io/MLRepo/ 上获得)，这是一个公共的基于网络的存储库，其中包含来自 15 个已发布的人类微生物组数据集的 33 个经过精心整理的分类和回归任务。我们强调了在几个用例中使用 ML Repo 的情况，以展示其广泛的应用，我们希望它成为算法开发人员的重要资源。
