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,Nap Bram,Nedyalkova Miroslava,Paciência Inês,Pasic Lejla,Pujolassos Meritxell,Shigdel Rajesh,Susín Antonio,Thiele Ines,Truică Ciprian-Octavian,Wilmes Paul,Yilmaz Ercument,Yousef Malik,Claesson Marcus Joakim,Truu Jaak,Carrillo de Santa Pau Enrique
Abstract
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.
Subject
Microbiology (medical),Microbiology