2026-04-28T22:57:14Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/182682024-02-19T15:25:15Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927col_20.500.12105_16968

00925njm 22002777a 4500 dc Moreno-Indias, Isabel author Lahti, Leo author Nedyalkova, Miroslava author Elbere, Ilze author Roshchupkin, Gennady author Adilovic, Muhamed author Aydemir, Onder author Bakir-Gungor, Burcu author Santa Pau, Enrique Carrillo-de author D'Elia, Domenica author Desai, Mahesh S author Falquet, Laurent author Gundogdu, Aycan author Hron, Karel author Klammsteiner, Thomas author Lopes, Marta B author Marcos-Zambrano, Laura Judith author Marques, Cláudia author Mason, Michael author May, Patrick author Pašić, Lejla author Pio, Gianvito author Pongor, Sándor author Promponas, Vasilis J author Przymus, Piotr author Saez-Rodriguez, Julio author Sampri, Alexia author Shigdel, Rajesh author Stres, Blaz author Suharoschi, Ramona author Truu, Jaak author Truică, Ciprian-Octavian author Vilne, Baiba author Vlachakis, Dimitrios author Yilmaz, Ercument author Zeller, Georg author Zomer, Aldert L author Gómez-Cabrero, David author Claesson, Marcus J author 2021-02-22 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. 1664-302X http://hdl.handle.net/10668/17334 http://hdl.handle.net/20.500.12105/18268 33692771 10.3389/fmicb.2021.635781 Frontiers in microbiology ML4Microbiome biomarker identification machine learning microbiome personalized medicine Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.