Microbiome learning Repo (ML Repo): A public repository of microbiome regression and classification tasks

Pajau Vangay; Benjamin M. Hillmann; Dan Knights

doi:10.1093/gigascience/giz042

Microbiome learning Repo (ML Repo): A public repository of microbiome regression and classification tasks

Pajau Vangay, Benjamin M. Hillmann, Dan Knights

Biotechnology Institute

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

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.

Original language	English (US)
Journal	GigaScience
Volume	8
Issue number	5
DOIs	https://doi.org/10.1093/gigascience/giz042
State	Published - May 1 2019

Bibliographical note

Funding Information:
This work is supported by funds from National Institutes of Health grant R01AI121383.

Publisher Copyright:
© The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords

Database
Machine learning
Microbiome
Repository

Publisher link

10.1093/gigascience/giz042

Find It

Find It at U of M Libraries

Cite this

@article{cc17bd6adc214f6788eaa9b2da832192,

title = "Microbiome learning Repo (ML Repo): A public repository of microbiome regression and classification tasks",

abstract = "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.",

keywords = "Database, Machine learning, Microbiome, Repository",

author = "Pajau Vangay and Hillmann, {Benjamin M.} and Dan Knights",

note = "Funding Information: This work is supported by funds from National Institutes of Health grant R01AI121383. Publisher Copyright: {\textcopyright} The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.",

year = "2019",

month = may,

day = "1",

doi = "10.1093/gigascience/giz042",

language = "English (US)",

volume = "8",

journal = "GigaScience",

issn = "2047-217X",

publisher = "BioMed Central",

number = "5",

}

TY - JOUR

T1 - Microbiome learning Repo (ML Repo)

T2 - A public repository of microbiome regression and classification tasks

AU - Vangay, Pajau

AU - Hillmann, Benjamin M.

AU - Knights, Dan

N1 - Funding Information: This work is supported by funds from National Institutes of Health grant R01AI121383. Publisher Copyright: © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - Database

KW - Machine learning

KW - Microbiome

KW - Repository

UR - http://www.scopus.com/inward/record.url?scp=85065480522&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85065480522&partnerID=8YFLogxK

U2 - 10.1093/gigascience/giz042

DO - 10.1093/gigascience/giz042

M3 - Article

C2 - 31042284

AN - SCOPUS:85065480522

VL - 8

JO - GigaScience

JF - GigaScience

SN - 2047-217X

IS - 5

ER -

Microbiome learning Repo (ML Repo): A public repository of microbiome regression and classification tasks

Abstract

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this