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Predicting microbiome compositions from species assemblages through deep learning.

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Title: Predicting microbiome compositions from species assemblages through deep learning.
Authors: Michel-Mata S; Center for Applied Physics and Advanced Technology, Universidad Nacional Autónoma de México, Juriquilla 76230, México.; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA., Wang XW; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA., Liu YY; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA., Angulo MT; CONACyT - Institute of Mathematics, Universidad Nacional Autónoma de México, Juriquilla 76230, México.
Source: IMeta [Imeta] 2022 Mar; Vol. 1 (1). Date of Electronic Publication: 2022 Mar 01.
Publication Type: Journal Article
Journal Info: Publisher: John Wiley & Sons Australia, Ltd on behalf of iMeta Science Country of Publication: Australia NLM ID: 9918350383706676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2770-596X (Electronic) Linking ISSN: 27705986 NLM ISO Abbreviation: Imeta Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Michel-Mata+S%22">Michel-Mata S</searchLink>; Center for Applied Physics and Advanced Technology, Universidad Nacional Autónoma de México, Juriquilla 76230, México.; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.<br /><searchLink fieldCode="AU" term="%22Wang+XW%22">Wang XW</searchLink>; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.<br /><searchLink fieldCode="AU" term="%22Liu+YY%22">Liu YY</searchLink>; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.<br /><searchLink fieldCode="AU" term="%22Angulo+MT%22">Angulo MT</searchLink>; CONACyT - Institute of Mathematics, Universidad Nacional Autónoma de México, Juriquilla 76230, México.
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  Data: <searchLink fieldCode="JN" term="%229918350383706676%22">IMeta</searchLink> [Imeta] 2022 Mar; Vol. 1 (1). <i>Date of Electronic Publication: </i>2022 Mar 01.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22John+Wiley+%26+Sons+Australia%2C+Ltd+on+behalf+of+iMeta+Science%22">John Wiley & Sons Australia, Ltd on behalf of iMeta Science </searchLink><i>Country of Publication: </i>Australia <i>NLM ID: </i>9918350383706676 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2770-596X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2227705986%22">27705986 </searchLink><i>NLM ISO Abbreviation: </i>Imeta <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.1002/imt2.3
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        Text: English
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      – TitleFull: Predicting microbiome compositions from species assemblages through deep learning.
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              Text: 2022 Mar
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              Y: 2022
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