McCoubrey, LE;
Elbadawi, M;
Orlu, M;
Gaisford, S;
Basit, AW;
(2021)
Harnessing machine learning for development of microbiome therapeutics.
Gut Microbes
, 13
(1)
pp. 1-20.
10.1080/19490976.2021.1872323.
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Abstract
The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.
| Type: | Article |
|---|---|
| Title: | Harnessing machine learning for development of microbiome therapeutics |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/19490976.2021.1872323 |
| Publisher version: | https://doi.org/10.1080/19490976.2021.1872323 |
| Language: | English |
| Additional information: | © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | COVID-19, artificial intelligence, clinical translation, colonic drug delivery, drug product development, machine learning, microbial therapeutics, microbiome, personalized medicines, pharmaceutical sciences |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10120864 |
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