[en] The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group)
Disciplines :
Environmental sciences & ecology Microbiology
Author, co-author :
Marcos-Zambrano, Laura Judith
López-Molina, Víctor Manuel
Bakir-Gungor, Burcu
Frohme, Marcus
Karaduzovic-Hadziabdic, Kanita
Klammsteiner, Thomas
Ibrahimi, Eliana
Lahti, Leo
Loncar-Turukalo, Tatjana
Dhamo, Xhilda
Simeon, Andrea
Nechyporenko, Alina
Pio, Gianvito
Przymus, Piotr
Sampri, Alexia
Trajkovik, Vladimir
Lacruz-Pleguezuelos, Blanca
Aasmets, Oliver
Araujo, Ricardo
Anagnostopoulos, Ioannis
Aydemir, Önder
Berland, Magali
Calle, M. Luz
Ceci, Michelangelo
Duman, Hatice
Gündoğdu, Aycan
Havulinna, Aki S.
Kaka Bra, Kardokh Hama Najib
Kalluci, Eglantina
Karav, Sercan
Lode, Daniel
Lopes, Marta B.
MAY, Patrick ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
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