Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

Petri Vänni; Mysore V Tejesvi; Niko Paalanne; Kjersti Aagaard; Gail Ackermann; Carlos A Camargo; Merete Eggesbø; Kohei Hasegawa; Anne G Hoen; Margaret R Karagas; Kaija-Leena Kolho; Martin F Laursen; Johnny Ludvigsson; Juliette Madan; Dennis Ownby; Catherine Stanton; Jakob Stokholm; Terhi Tapiainen

doi:10.1128/msystems.00364-23

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

Petri Vänni, Mysore V Tejesvi, Niko Paalanne, Kjersti Aagaard, Gail Ackermann, Carlos A Camargo, Merete Eggesbø, Kohei Hasegawa, Anne G Hoen, Margaret R Karagas, Kaija-Leena Kolho, Martin F Laursen, Johnny Ludvigsson, Juliette Madan, Dennis Ownby, Catherine Stanton, Jakob Stokholm, Terhi Tapiainen

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2 Citationer (Scopus)

Abstract

There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

Originalsprog	Engelsk
Tidsskrift	mSystems
Vol/bind	8
Udgave nummer	6
Sider (fra-til)	e0036423
ISSN	2379-5077
DOI	https://doi.org/10.1128/msystems.00364-23
Status	Udgivet - 21 dec. 2023

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Vänni, P., Tejesvi, M. V., Paalanne, N., Aagaard, K., Ackermann, G., Camargo, C. A., Eggesbø, M., Hasegawa, K., Hoen, A. G., Karagas, M. R., Kolho, K.-L., Laursen, M. F., Ludvigsson, J., Madan, J., Ownby, D., Stanton, C., Stokholm, J., & Tapiainen, T. (2023). Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts. mSystems, 8(6), e0036423. https://doi.org/10.1128/msystems.00364-23

Vänni, P, Tejesvi, MV, Paalanne, N, Aagaard, K, Ackermann, G, Camargo, CA, Eggesbø, M, Hasegawa, K, Hoen, AG, Karagas, MR, Kolho, K-L, Laursen, MF, Ludvigsson, J, Madan, J, Ownby, D, Stanton, C, Stokholm, J & Tapiainen, T 2023, 'Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts', mSystems, bind 8, nr. 6, s. e0036423. https://doi.org/10.1128/msystems.00364-23

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abstract = "There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.",

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AU - Vänni, Petri

AU - Tejesvi, Mysore V

AU - Paalanne, Niko

AU - Aagaard, Kjersti

AU - Ackermann, Gail

AU - Camargo, Carlos A

AU - Eggesbø, Merete

AU - Hasegawa, Kohei

AU - Hoen, Anne G

AU - Karagas, Margaret R

AU - Kolho, Kaija-Leena

AU - Laursen, Martin F

AU - Ludvigsson, Johnny

AU - Madan, Juliette

AU - Ownby, Dennis

AU - Stanton, Catherine

AU - Stokholm, Jakob

AU - Tapiainen, Terhi

PY - 2023/12/21

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N2 - There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

AB - There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

KW - Feces

KW - Gastrointestinal Microbiome/genetics

KW - Humans

KW - Infant

KW - Machine Learning

KW - Microbiota

KW - RNA, Ribosomal, 16S/genetics

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VL - 8

SP - e0036423

JO - mSystems

JF - mSystems

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ER -

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

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