A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

Le Goallec, Alan; Tierney, Braden T; Luber, Jacob M; Cofer, Evan M; Kostic, Aleksandar D; Patel, Chirag J

Le Goallec, Alan; Tierney, Braden T; Luber, Jacob M; Cofer, Evan M; Kostic, Aleksandar D; Patel, Chirag J.

Affiliation

Le Goallec A; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
Tierney BT; Department of Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America.
Luber JM; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
Cofer EM; Section on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, United States of America.
Kostic AD; Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Boston, Massachusetts, United States of America.
Patel CJ; Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts, United States of America.

PLoS Comput Biol ; 16(5): e1007895, 2020 05.

Article in En | MEDLINE | ID: mdl-32392251

Subject(s)

Anti-Bacterial Agents/administration & dosage; Breast Feeding; Machine Learning; Metagenomics; Algorithms; Female; Geography; Humans; Infant; Male; Models, Theoretical

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Feeding / Metagenomics / Machine Learning / Anti-Bacterial Agents Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Infant / Male Language: En Journal: PLoS Comput Biol Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: United States Country of publication: United States

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