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For: Reiman D, Layden BT, Dai Y. MiMeNet: Exploring microbiome-metabolome relationships using neural networks. PLoS Comput Biol 2021;17:e1009021. [PMID: 33999922 DOI: 10.1371/journal.pcbi.1009021] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 12.0] [Reference Citation Analysis]
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6 Lee C, Amini F, Hu G, Halverson LJ. Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure. Front Microbiol 2022;13:899565. [DOI: 10.3389/fmicb.2022.899565] [Reference Citation Analysis]
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8 Leonhardt SD, Peters B, Keller A. Do amino and fatty acid profiles of pollen provisions correlate with bacterial microbiomes in the mason bee Osmia bicornis? Philos Trans R Soc Lond B Biol Sci 2022;377:20210171. [PMID: 35491605 DOI: 10.1098/rstb.2021.0171] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Taneishi K, Tsuchiya Y. Structure-based analyses of gut microbiome-related proteins by neural networks and molecular dynamics simulations. Current Opinion in Structural Biology 2022;73:102336. [DOI: 10.1016/j.sbi.2022.102336] [Reference Citation Analysis]
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11 Curry KD, Nute MG, Treangen TJ. It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. Emerg Top Life Sci 2021;5:815-27. [PMID: 34779841 DOI: 10.1042/ETLS20210213] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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13 Noecker C, Eng A, Borenstein E. MIMOSA2: A metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data.. [DOI: 10.1101/2021.09.14.459910] [Reference Citation Analysis]
14 Khajeh T, Reiman D, Morley R, Dai Y. Integrating microbiome and metabolome data for host disease prediction via deep neural networks. 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) 2021. [DOI: 10.1109/bhi50953.2021.9508601] [Reference Citation Analysis]