Advancing microbiome research with machine learning
| Autor(a) principal: | |
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| Data de Publicação: | 2023 |
| Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/164896 |
Resumo: | 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. |
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Advancing microbiome research with machine learningkey findings from the ML4Microbiome COST actionartificial intelligencebest practicesmachine learningmicrobiomestandardsMicrobiologyMicrobiology (medical)SDG 3 - Good Health and Well-beingFunding 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.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.CMA - Centro de Matemática e AplicaçõesUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e IndustrialDEMI - Departamento de Engenharia Mecânica e IndustrialRUND’Elia, DomenicaTruu, JaakLahti, LeoBerland, MagaliPapoutsoglou, GeorgiosCeci, MichelangeloZomer, AldertLopes, Marta B.Ibrahimi, ElianaGruca, AleksandraNechyporenko, AlinaFrohme, MarcusKlammsteiner, ThomasPau, Enrique Carrillo de SantaMarcos-Zambrano, Laura JudithHron, KarelPio, GianvitoSimeon, AndreaSuharoschi, RamonaMoreno-Indias, IsabelTemko, AndriyNedyalkova, MiroslavaApostol, Elena SimonaTruică, Ciprian OctavianShigdel, RajeshTelalović, Jasminka HasićBongcam-Rudloff, ErikPrzymus, PiotrJordamović, Naida BabićFalquet, LaurentTarazona, SoniaSampri, AlexiaIsola, GaetanoPérez-Serrano, DavidTrajkovik, VladimirKlucar, LubosLoncar-Turukalo, TatjanaHavulinna, Aki S.Jansen, ChristianBertelsen, Randi J.Claesson, Marcus Joakim2024-03-13T23:56:11Z2023-092023-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article8application/pdfhttp://hdl.handle.net/10362/164896eng1664-302XPURE: 84354777https://doi.org/10.3389/fmicb.2023.1257002info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-18T01:47:13Zoai:run.unl.pt:10362/164896Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:02:03.817111Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Advancing microbiome research with machine learning key findings from the ML4Microbiome COST action |
| title |
Advancing microbiome research with machine learning |
| spellingShingle |
Advancing microbiome research with machine learning D’Elia, Domenica artificial intelligence best practices machine learning microbiome standards Microbiology Microbiology (medical) SDG 3 - Good Health and Well-being |
| title_short |
Advancing microbiome research with machine learning |
| title_full |
Advancing microbiome research with machine learning |
| title_fullStr |
Advancing microbiome research with machine learning |
| title_full_unstemmed |
Advancing microbiome research with machine learning |
| title_sort |
Advancing microbiome research with machine learning |
| author |
D’Elia, Domenica |
| author_facet |
D’Elia, Domenica Truu, Jaak Lahti, Leo Berland, Magali Papoutsoglou, Georgios Ceci, Michelangelo Zomer, Aldert Lopes, Marta B. Ibrahimi, Eliana Gruca, Aleksandra Nechyporenko, Alina Frohme, Marcus Klammsteiner, Thomas Pau, Enrique Carrillo de Santa Marcos-Zambrano, Laura Judith Hron, Karel Pio, Gianvito Simeon, Andrea Suharoschi, Ramona Moreno-Indias, Isabel Temko, Andriy Nedyalkova, Miroslava Apostol, Elena Simona Truică, Ciprian Octavian Shigdel, Rajesh Telalović, Jasminka Hasić Bongcam-Rudloff, Erik Przymus, Piotr Jordamović, Naida Babić Falquet, Laurent Tarazona, Sonia Sampri, Alexia Isola, Gaetano Pérez-Serrano, David Trajkovik, Vladimir Klucar, Lubos Loncar-Turukalo, Tatjana Havulinna, Aki S. Jansen, Christian Bertelsen, Randi J. Claesson, Marcus Joakim |
| author_role |
author |
| author2 |
Truu, Jaak Lahti, Leo Berland, Magali Papoutsoglou, Georgios Ceci, Michelangelo Zomer, Aldert Lopes, Marta B. Ibrahimi, Eliana Gruca, Aleksandra Nechyporenko, Alina Frohme, Marcus Klammsteiner, Thomas Pau, Enrique Carrillo de Santa Marcos-Zambrano, Laura Judith Hron, Karel Pio, Gianvito Simeon, Andrea Suharoschi, Ramona Moreno-Indias, Isabel Temko, Andriy Nedyalkova, Miroslava Apostol, Elena Simona Truică, Ciprian Octavian Shigdel, Rajesh Telalović, Jasminka Hasić Bongcam-Rudloff, Erik Przymus, Piotr Jordamović, Naida Babić Falquet, Laurent Tarazona, Sonia Sampri, Alexia Isola, Gaetano Pérez-Serrano, David Trajkovik, Vladimir Klucar, Lubos Loncar-Turukalo, Tatjana Havulinna, Aki S. Jansen, Christian Bertelsen, Randi J. Claesson, Marcus Joakim |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
CMA - Centro de Matemática e Aplicações UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial DEMI - Departamento de Engenharia Mecânica e Industrial RUN |
| dc.contributor.author.fl_str_mv |
D’Elia, Domenica Truu, Jaak Lahti, Leo Berland, Magali Papoutsoglou, Georgios Ceci, Michelangelo Zomer, Aldert Lopes, Marta B. Ibrahimi, Eliana Gruca, Aleksandra Nechyporenko, Alina Frohme, Marcus Klammsteiner, Thomas Pau, Enrique Carrillo de Santa Marcos-Zambrano, Laura Judith Hron, Karel Pio, Gianvito Simeon, Andrea Suharoschi, Ramona Moreno-Indias, Isabel Temko, Andriy Nedyalkova, Miroslava Apostol, Elena Simona Truică, Ciprian Octavian Shigdel, Rajesh Telalović, Jasminka Hasić Bongcam-Rudloff, Erik Przymus, Piotr Jordamović, Naida Babić Falquet, Laurent Tarazona, Sonia Sampri, Alexia Isola, Gaetano Pérez-Serrano, David Trajkovik, Vladimir Klucar, Lubos Loncar-Turukalo, Tatjana Havulinna, Aki S. Jansen, Christian Bertelsen, Randi J. Claesson, Marcus Joakim |
| dc.subject.por.fl_str_mv |
artificial intelligence best practices machine learning microbiome standards Microbiology Microbiology (medical) SDG 3 - Good Health and Well-being |
| topic |
artificial intelligence best practices machine learning microbiome standards Microbiology Microbiology (medical) SDG 3 - Good Health and Well-being |
| description |
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. |
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2023 |
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2023-09 2023-09-01T00:00:00Z 2024-03-13T23:56:11Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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http://hdl.handle.net/10362/164896 |
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eng |
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eng |
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1664-302X PURE: 84354777 https://doi.org/10.3389/fmicb.2023.1257002 |
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8 application/pdf |
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