TY - JOUR
T1 - Advancing microbiome research with machine learning
T2 - key findings from the ML4Microbiome COST action
AU - D’Elia, Domenica
AU - Truu, Jaak
AU - Lahti, Leo
AU - Berland, Magali
AU - Papoutsoglou, Georgios
AU - Ceci, Michelangelo
AU - Zomer, Aldert
AU - Lopes, Marta B.
AU - Ibrahimi, Eliana
AU - Gruca, Aleksandra
AU - Nechyporenko, Alina
AU - Frohme, Marcus
AU - Klammsteiner, Thomas
AU - Pau, Enrique Carrillo de Santa
AU - Marcos-Zambrano, Laura Judith
AU - Hron, Karel
AU - Pio, Gianvito
AU - Simeon, Andrea
AU - Suharoschi, Ramona
AU - Moreno-Indias, Isabel
AU - Temko, Andriy
AU - Nedyalkova, Miroslava
AU - Apostol, Elena Simona
AU - Truică, Ciprian Octavian
AU - Shigdel, Rajesh
AU - Telalović, Jasminka Hasić
AU - Bongcam-Rudloff, Erik
AU - Przymus, Piotr
AU - Jordamović, Naida Babić
AU - Falquet, Laurent
AU - Tarazona, Sonia
AU - Sampri, Alexia
AU - Isola, Gaetano
AU - Pérez-Serrano, David
AU - Trajkovik, Vladimir
AU - Klucar, Lubos
AU - Loncar-Turukalo, Tatjana
AU - Havulinna, Aki S.
AU - Jansen, Christian
AU - Bertelsen, Randi J.
AU - Claesson, Marcus Joakim
N1 - Funding Information:
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies” (CA18131), supported by COST (European Cooperation in Science and Technology), www.cost.eu . MB acknowledges support through the Metagenopolis grant ANR-11-DPBS-0001. IM-I acknowledges support by the “Miguel Servet Type II” program (CPII21/00013) of the ISCIII-Madrid (Spain), co-financed by the FEDER.
Publisher Copyright:
Copyright © 2023 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson.
PY - 2023/9
Y1 - 2023/9
N2 - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
AB - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
KW - artificial intelligence
KW - best practices
KW - machine learning
KW - microbiome
KW - standards
UR - http://www.scopus.com/inward/record.url?scp=85173952003&partnerID=8YFLogxK
U2 - 10.3389/fmicb.2023.1257002
DO - 10.3389/fmicb.2023.1257002
M3 - Article
C2 - 37808321
AN - SCOPUS:85173952003
SN - 1664-302X
VL - 14
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
M1 - 1257002
ER -