@article{MarcosZambranoLopezMolinaBakirGungoretal.2023, author = {Marcos-Zambrano, Laura Judith and L{\´o}pez-Molina, V{\´i}ctor Manuel and Bakir-Gungor, Burcu and Frohme, Marcus and Karaduzovic-Hadziabdic, Kanita and Klammsteiner, Thomas and Ibrahimi, Eliana and Lahti, Leo and Loncar-Turukalo, Tatjana and Dhamo, Xhilda and Simeon, Andrea and Nechyporenko, Alina and Pio, Gianvito and Przymus, Piotr and Sampri, Alexia and Trajkovik, Vladimir and Lacruz-Pleguezuelos, Blanca and Aasmets, Oliver and Araujo, Ricardo and Anagnostopoulos, Ioannis and Aydemir, {\"O}nder and Berland, Magali and Calle, M. Luz and Ceci, Michelangelo and Duman, Hatice and G{\"u}ndoğdu, Aycan and Havulinna, Aki S. and Kaka Bra, Kardokh Hama Najib and Kalluci, Eglantina and Karav, Sercan and Lode, Daniel and Lopes, Marta B. and May, Patrick and Nap, Bram and Nedyalkova, Miroslava and Paci{\^e}ncia, In{\^e}s and Pasic, Lejla and Pujolassos, Meritxell and Shigdel, Rajesh and Sus{\´i}n, Antonio and Thiele, Ines and Truică, Ciprian-Octavian and Wilmes, Paul and Yilmaz, Ercument and Yousef, Malik and Claesson, Marcus Joakim and Truu, Jaak and Carrillo de Santa Pau, Enrique}, title = {A toolbox of machine learning software to support microbiome analysis}, series = {Frontiers in Microbiology}, volume = {14}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18271}, year = {2023}, 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.}, language = {en} }