A toolbox of machine learning software to support microbiome analysis
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 (2023-11-22)
Frontiers Research Foundation
22.11.2023
Marcos-Zambrano LJ, López-Molina VM, Bakir-Gungor B, Frohme M, Karaduzovic-Hadziabdic K, Klammsteiner T, Ibrahimi E, Lahti L, Loncar-Turukalo T, Dhamo X, Simeon A, Nechyporenko A, Pio G, Przymus P, Sampri A, Trajkovik V, Lacruz-Pleguezuelos B, Aasmets O, Araujo R, Anagnostopoulos I, Aydemir &, Berland M, Calle ML, Ceci M, Duman H, Gündoğdu A, Havulinna AS, Kaka Bra KHN, Kalluci E, Karav S, Lode D, Lopes MB, May P, Nap B, Nedyalkova M, Paciência I, Pasic L, Pujolassos M, Shigdel R, Susín A, Thiele I, Truică C-O, Wilmes P, Yilmaz E, Yousef M, Claesson MJ, Truu J and Carrillo de Santa Pau E (2023) A toolbox of machine learning software to support microbiome analysis. Front. Microbiol. 14:1250806. doi: 10.3389/fmicb.2023.1250806
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© 2023 Marcos-Zambrano, López-Molina, Bakir-Gungor, Frohme, Karaduzovic-Hadziabdic, Klammsteiner, Ibrahimi, Lahti, Loncar Turukalo, Dhamo, Simeon, Nechyporenko, Pio, Przymus, Sampri, Trajkovik, Lacruz-Pleguezuelos, Aasmets, Araujo, Anagnostopoulos, Aydemir, Berland, Calle, Ceci, Duman, Gündoğdu, Havulinna, Kaka Bra, Kalluci, Karav, Lode, Lopes, May, Nap, Nedyalkova, Paciência, Pasic, Pujolassos, Shigdel, Susín, Thiele, Truică, Wilmes, Yilmaz, Yousef, Claesson, Truu, Carrillo de Santa Pau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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© 2023 Marcos-Zambrano, López-Molina, Bakir-Gungor, Frohme, Karaduzovic-Hadziabdic, Klammsteiner, Ibrahimi, Lahti, Loncar Turukalo, Dhamo, Simeon, Nechyporenko, Pio, Przymus, Sampri, Trajkovik, Lacruz-Pleguezuelos, Aasmets, Araujo, Anagnostopoulos, Aydemir, Berland, Calle, Ceci, Duman, Gündoğdu, Havulinna, Kaka Bra, Kalluci, Karav, Lode, Lopes, May, Nap, Nedyalkova, Paciência, Pasic, Pujolassos, Shigdel, Susín, Thiele, Truică, Wilmes, Yilmaz, Yousef, Claesson, Truu, Carrillo de Santa Pau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202401101176
https://urn.fi/URN:NBN:fi:oulu-202401101176
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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.
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.
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