Unlocking the microbial studies through computational approaches: how far have we reached?
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-023-26220-0.pdf
Reference149 articles.
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3. Andrighetti T, Bohar B, Lemke N, Sudhakar P, Korcsmaros T (2020) MicrobioLink: an integrated computational pipeline to infer functional effects of microbiome–host interactions. Cells 9:1278. https://doi.org/10.3390/cells9051278
4. Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L (2018) DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6:23. https://doi.org/10.1186/s40168-018-0401-z
5. Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J, Glasner C, Feil EJ, Holden MTG, Yeats CA, Grundmann H, Spratt BG, Aanensen DM (2016) Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2(11):e000093. https://doi.org/10.1099/mgen.0.000093
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