RT Journal Article
T1 Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.
A1 Moreno-Indias, Isabel
A1 Lahti, Leo
A1 Nedyalkova, Miroslava
A1 Elbere, Ilze
A1 Roshchupkin, Gennady
A1 Adilovic, Muhamed
A1 Aydemir, Onder
A1 Bakir-Gungor, Burcu
A1 Santa Pau, Enrique Carrillo-de
A1 D'Elia, Domenica
A1 Desai, Mahesh S
A1 Falquet, Laurent
A1 Gundogdu, Aycan
A1 Hron, Karel
A1 Klammsteiner, Thomas
A1 Lopes, Marta B
A1 Marcos-Zambrano, Laura Judith
A1 Marques, Cláudia
A1 Mason, Michael
A1 May, Patrick
A1 Pašić, Lejla
A1 Pio, Gianvito
A1 Pongor, Sándor
A1 Promponas, Vasilis J
A1 Przymus, Piotr
A1 Saez-Rodriguez, Julio
A1 Sampri, Alexia
A1 Shigdel, Rajesh
A1 Stres, Blaz
A1 Suharoschi, Ramona
A1 Truu, Jaak
A1 Truică, Ciprian-Octavian
A1 Vilne, Baiba
A1 Vlachakis, Dimitrios
A1 Yilmaz, Ercument
A1 Zeller, Georg
A1 Zomer, Aldert L
A1 Gómez-Cabrero, David
A1 Claesson, Marcus J
K1 ML4Microbiome
K1 biomarker identification
K1 machine learning
K1 microbiome
K1 personalized medicine
AB 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 "ML4Microbiome" 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.
SN 1664-302X
YR 2021
FD 2021-02-22
LK http://hdl.handle.net/10668/17334
UL http://hdl.handle.net/10668/17334
LA en
DS RISalud
RD Apr 10, 2025