The diagnostic potential and barriers of microbiome based therapeutics

Animesh Acharjee*, Utpreksha Singh, Saptamita Paul Choudhury, Georgios Gkoutos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The ‘omics’ technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.
Original languageEnglish
Number of pages10
JournalDiagnosis
Early online date25 Aug 2022
DOIs
Publication statusE-pub ahead of print - 25 Aug 2022

Bibliographical note

Funding Information:
Research funding: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032), MAESTRIA (Grant agreement ID 965286) and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.

Publisher Copyright:
© 2022 the author(s), published by De Gruyter, Berlin/Boston 2022.

Keywords

  • biomarker
  • microbiota
  • Machine learning;
  • Diagnostics

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