Statistical and machine learning techniques in human microbiome studies: Contemporary challenges and solutions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73610051" target="_blank" >RIV/61989592:15310/21:73610051 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/fmicb.2021.635781/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fmicb.2021.635781/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fmicb.2021.635781" target="_blank" >10.3389/fmicb.2021.635781</a>

Result language

angličtina

Original language name

Statistical and machine learning techniques in human microbiome studies: Contemporary challenges and solutions

Original language description

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 &quot;ML4Microbiome&quot; that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10102 - Applied mathematics

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Microbiology

ISSN

1664-302X

e-ISSN

—

Volume of the periodical

12

Issue of the periodical within the volume

FEB

Country of publishing house

CH - SWITZERLAND

Number of pages

9

Pages from-to

"635781-1"-"635781-9"

UT code for WoS article

000625979400001

EID of the result in the Scopus database

2-s2.0-85102370593