{"pmid":1,"quality_controlled":"1","date_published":"2023-09-25T00:00:00Z","volume":14,"scopus_import":"1","ddc":["000"],"publication_identifier":{"eissn":["1664-302X"]},"article_processing_charge":"Yes","intvolume":"        14","title":"Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action","citation":{"ista":"D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau ECDS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol ES, Truică CO, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ, Claesson MJ. 2023. Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action. Frontiers in Microbiology. 14, 1257002.","mla":"D’Elia, Domenica, et al. “Advancing Microbiome Research with Machine Learning: Key Findings from the ML4Microbiome COST Action.” <i>Frontiers in Microbiology</i>, vol. 14, 1257002, Frontiers, 2023, doi:<a href=\"https://doi.org/10.3389/fmicb.2023.1257002\">10.3389/fmicb.2023.1257002</a>.","apa":"D’Elia, D., Truu, J., Lahti, L., Berland, M., Papoutsoglou, G., Ceci, M., … Claesson, M. J. (2023). Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action. <i>Frontiers in Microbiology</i>. Frontiers. <a href=\"https://doi.org/10.3389/fmicb.2023.1257002\">https://doi.org/10.3389/fmicb.2023.1257002</a>","ama":"D’Elia D, Truu J, Lahti L, et al. Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action. <i>Frontiers in Microbiology</i>. 2023;14. doi:<a href=\"https://doi.org/10.3389/fmicb.2023.1257002\">10.3389/fmicb.2023.1257002</a>","chicago":"D’Elia, Domenica, Jaak Truu, Leo Lahti, Magali Berland, Georgios Papoutsoglou, Michelangelo Ceci, Aldert Zomer, et al. “Advancing Microbiome Research with Machine Learning: Key Findings from the ML4Microbiome COST Action.” <i>Frontiers in Microbiology</i>. Frontiers, 2023. <a href=\"https://doi.org/10.3389/fmicb.2023.1257002\">https://doi.org/10.3389/fmicb.2023.1257002</a>.","short":"D. D’Elia, J. Truu, L. Lahti, M. Berland, G. Papoutsoglou, M. Ceci, A. Zomer, M.B. Lopes, E. Ibrahimi, A. Gruca, A. Nechyporenko, M. Frohme, T. Klammsteiner, E.C.D.S. Pau, L.J. Marcos-Zambrano, K. Hron, G. Pio, A. Simeon, R. Suharoschi, I. Moreno-Indias, A. Temko, M. Nedyalkova, E.S. Apostol, C.O. Truică, R. Shigdel, J.H. Telalović, E. Bongcam-Rudloff, P. Przymus, N.B. Jordamović, L. Falquet, S. Tarazona, A. Sampri, G. Isola, D. Pérez-Serrano, V. Trajkovik, L. Klucar, T. Loncar-Turukalo, A.S. Havulinna, C. Jansen, R.J. Bertelsen, M.J. Claesson, Frontiers in Microbiology 14 (2023).","ieee":"D. D’Elia <i>et al.</i>, “Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action,” <i>Frontiers in Microbiology</i>, vol. 14. Frontiers, 2023."},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"article_number":"1257002","has_accepted_license":"1","language":[{"iso":"eng"}],"day":"25","author":[{"full_name":"D’Elia, Domenica","first_name":"Domenica","last_name":"D’Elia"},{"full_name":"Truu, Jaak","first_name":"Jaak","last_name":"Truu"},{"last_name":"Lahti","first_name":"Leo","full_name":"Lahti, Leo"},{"last_name":"Berland","first_name":"Magali","full_name":"Berland, Magali"},{"full_name":"Papoutsoglou, Georgios","first_name":"Georgios","last_name":"Papoutsoglou"},{"full_name":"Ceci, Michelangelo","last_name":"Ceci","first_name":"Michelangelo"},{"full_name":"Zomer, Aldert","first_name":"Aldert","last_name":"Zomer"},{"first_name":"Marta B.","last_name":"Lopes","full_name":"Lopes, Marta B."},{"first_name":"Eliana","last_name":"Ibrahimi","full_name":"Ibrahimi, Eliana"},{"full_name":"Gruca, Aleksandra","first_name":"Aleksandra","last_name":"Gruca"},{"first_name":"Alina","last_name":"Nechyporenko","full_name":"Nechyporenko, Alina"},{"last_name":"Frohme","first_name":"Marcus","full_name":"Frohme, Marcus"},{"full_name":"Klammsteiner, Thomas","first_name":"Thomas","last_name":"Klammsteiner"},{"full_name":"Pau, Enrique Carrillo De Santa","last_name":"Pau","first_name":"Enrique Carrillo De Santa"},{"last_name":"Marcos-Zambrano","first_name":"Laura Judith","full_name":"Marcos-Zambrano, Laura Judith"},{"first_name":"Karel","last_name":"Hron","full_name":"Hron, Karel"},{"last_name":"Pio","first_name":"Gianvito","full_name":"Pio, Gianvito"},{"full_name":"Simeon, Andrea","last_name":"Simeon","first_name":"Andrea"},{"full_name":"Suharoschi, Ramona","first_name":"Ramona","last_name":"Suharoschi"},{"last_name":"Moreno-Indias","first_name":"Isabel","full_name":"Moreno-Indias, Isabel"},{"first_name":"Andriy","last_name":"Temko","full_name":"Temko, Andriy"},{"last_name":"Nedyalkova","first_name":"Miroslava","full_name":"Nedyalkova, Miroslava"},{"full_name":"Apostol, Elena Simona","last_name":"Apostol","first_name":"Elena Simona"},{"full_name":"Truică, Ciprian Octavian","first_name":"Ciprian Octavian","last_name":"Truică"},{"last_name":"Shigdel","first_name":"Rajesh","full_name":"Shigdel, Rajesh"},{"first_name":"Jasminka Hasić","last_name":"Telalović","full_name":"Telalović, Jasminka Hasić"},{"last_name":"Bongcam-Rudloff","first_name":"Erik","full_name":"Bongcam-Rudloff, Erik"},{"first_name":"Piotr","last_name":"Przymus","full_name":"Przymus, Piotr"},{"last_name":"Jordamović","first_name":"Naida Babić","full_name":"Jordamović, Naida Babić"},{"first_name":"Laurent","last_name":"Falquet","full_name":"Falquet, Laurent"},{"full_name":"Tarazona, Sonia","first_name":"Sonia","last_name":"Tarazona"},{"full_name":"Sampri, Alexia","first_name":"Alexia","last_name":"Sampri"},{"full_name":"Isola, Gaetano","first_name":"Gaetano","last_name":"Isola"},{"last_name":"Pérez-Serrano","first_name":"David","full_name":"Pérez-Serrano, David"},{"last_name":"Trajkovik","first_name":"Vladimir","full_name":"Trajkovik, Vladimir"},{"full_name":"Klucar, Lubos","first_name":"Lubos","last_name":"Klucar"},{"first_name":"Tatjana","last_name":"Loncar-Turukalo","full_name":"Loncar-Turukalo, Tatjana"},{"full_name":"Havulinna, Aki S.","last_name":"Havulinna","first_name":"Aki S."},{"first_name":"Christian","last_name":"Jansen","full_name":"Jansen, Christian","id":"837b2259-bcc9-11ed-a196-ae55927bc6e2"},{"full_name":"Bertelsen, Randi J.","last_name":"Bertelsen","first_name":"Randi J."},{"full_name":"Claesson, Marcus Joakim","first_name":"Marcus Joakim","last_name":"Claesson"}],"isi":1,"year":"2023","abstract":[{"text":"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.","lang":"eng"}],"oa":1,"article_type":"original","status":"public","file":[{"success":1,"date_updated":"2023-10-30T13:38:48Z","access_level":"open_access","creator":"dernst","file_size":505078,"date_created":"2023-10-30T13:38:48Z","file_name":"2023_FrontiersMicrobiology_DElia.pdf","checksum":"6c0acdd8fa111a699826957b8dff19d5","content_type":"application/pdf","relation":"main_file","file_id":"14471"}],"_id":"14449","acknowledgement":"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.\r\nThe authors are grateful to all COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies” members for their contribution to the COST Action objectives, and to COST (European Cooperation in Science and Technology) for the economic support, www.cost.eu. WG2 and WG3 thank Emmanuelle Le Chatelier and Pauline Barbet (Université Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France) for preparing the shotgun CRC benchmark dataset.","file_date_updated":"2023-10-30T13:38:48Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-12-13T13:07:21Z","type":"journal_article","department":[{"_id":"ScienComp"}],"doi":"10.3389/fmicb.2023.1257002","oa_version":"Published Version","publication":"Frontiers in Microbiology","publication_status":"published","month":"09","date_created":"2023-10-22T22:01:16Z","external_id":{"pmid":["37808321"],"isi":["001080536000001"]},"publisher":"Frontiers"}