AI-driven insights into the microbiota: Figuring out the mysterious world of the gut

Abhinandan Patil , Neha Singh , Mohsina Patwekar , Faheem Patwekar , Anasuya Patil , Jeetendra Kumar Gupta , Selvaraja Elumalai , Nagam Santhi Priya , Alapati sahithi

Intelligent Pharmacy ›› 2025, Vol. 3 ›› Issue (1) : 46 -52.

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Intelligent Pharmacy ›› 2025, Vol. 3 ›› Issue (1) : 46 -52. DOI: 10.1016/j.ipha.2024.08.003
Review article

AI-driven insights into the microbiota: Figuring out the mysterious world of the gut

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Abstract

This review delves into the fascinating realm of microbial insights enabled by artificial intelligence (AI), unveiling the mysteries of the intricate gut environment. Research into the human microbiome has evolved due to the fast development of AI and Machine learning (ML). Never before have such novel avenues for individualized medical care and therapeutic therapies been available as a result of this. Our first stop is at software developed specifically for microbiome data analysis. Complex datasets can be accessed and valuable information extracted using AI algorithms and machine learning approaches. Next, we take a look at predictive modeling of gut microbial interactions. Here we see how AI can foretell the actions of microorganisms and their effects on host health and illness. Afterwards, we investigate the efficacy of AI in detecting microbe biomarkers, which are crucial indicators of gut health and potential dangers of disease. A disease's root cause can be identified and a treatment strategy developed using this innovative approach. We also delve into the realm of personalized microbiome analysis and demonstrate how AI may assist in making dietary and lifestyle adjustments that are most suited to each individual in order to enhance their health. The impact of AI extends beyond the realm of research and assessment and include the development of novel medications. Our focus is on the ways AI is assisting the hunt for novel probiotics and microbiome-based therapies, which could one day lead to the development of effective remedies for various medical conditions. However, although we anticipate AI's potential, we must equally consider the ethical considerations involved in studying microbiota. This paper highlights the significance of data protection, transparency, and bias reduction in ensuring the responsible and fair use of AI. We can maximize AI's potential without trampling on people's rights or exacerbating existing inequalities if we adhere to ethical guidelines and work to earn the public's trust. Finally, this study demonstrates the potential power of AI-driven microbiome discoveries. By being committed to ethical principles and vigilant in our pursuit of new challenges, we may advance microbiota research toward a future of data-driven, customized healthcare that utilizes AI as a valuable tool for optimal health and wellness.

Keywords

AI / Microbiome / Algorithm / Probiotics / Medicines

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Abhinandan Patil, Neha Singh, Mohsina Patwekar, Faheem Patwekar, Anasuya Patil, Jeetendra Kumar Gupta, Selvaraja Elumalai, Nagam Santhi Priya, Alapati sahithi. AI-driven insights into the microbiota: Figuring out the mysterious world of the gut. Intelligent Pharmacy, 2025, 3(1): 46-52 DOI:10.1016/j.ipha.2024.08.003

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References

[1]

Toju H , Peay KG , Yamamichi M , et al. Core microbiomes for sustainable agroecosystems. Nat Plants. 2018 Apr 30; 4 (5): 247- 257.

[2]

Labbate M , Seymour JR , Lauro F , Brown MV . Editorial: anthropogenic impacts on the microbial ecology and function of aquatic environments. Front Microbiol [Internet]. 2016 Jul 6; 7. Accessed June 20, 2024.

[3]

Lozupone CA , Stombaugh JI , Gordon JI , Jansson JK , Knight R . Diversity, stability and resilience of the human gut microbiota. Nature. 2012 Sep; 489 (7415): 220- 230.

[4]

Müller DB , Vogel C , Bai Y , Vorholt JA . The plant microbiota: systems-level insights and perspectives. Annu Rev Genet. 2016 Nov 23; 50 (1): 211- 234.

[5]

Pita L , Rix L , Slaby BM , Franke A , Hentschel U . The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome. 2018 Dec; 6 (1): 46.

[6]

Alberdi A , Andersen SB , Limborg MT , Dunn RR , Gilbert MTP . Disentangling host-microbiota complexity through hologenomics. Nat Rev Genet. 2022 May; 23 (5): 281- 297.

[7]

Berg G , Rybakova D , Fischer D , et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020 Dec; 8 (1): 103.

[8]

Sessitsch A , Mitter B . 21st century agriculture: integration of plant microbiomes for improved crop production and food security. Microb Biotechnol. 2015 Jan; 8 (1): 32- 33.

[9]

Marcos-Zambrano LJ , Karaduzovic-Hadziabdic K , Loncar Turukalo T , et al. Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment. Front Microbiol. 2021 Feb 19; 12: 634511.

[10]

Gupta MM , Gupta A . Survey of artificial intelligence approaches in the study of anthropogenic impacts on symbiotic organisms-a holistic view. Symbiosis. 2021 Jul; 84 (3): 271- 283.

[11]

Albright MBN , Louca S , Winkler DE , et al. Solutions in microbiome engineering: prioritizing barriers to organism establishment. ISME J. 2022 Feb 1; 16 (2): 331- 338.

[12]

Lewis WH , Tahon G , Geesink P , Sousa DZ , Ettema TJG . Innovations to culturing the uncultured microbial majority. Nat Rev Microbiol. 2021 Apr; 19 (4): 225- 240.

[13]

Jiang X , Li X , Yang L , et al. How microbes shape their communities? A microbial community model based on functional genes. Dev Reprod Biol. 2019 Feb 1; 17 (1): 91- 105.

[14]

Liu YX , Qin Y , Chen T , et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell. 2021 May; 12 (5): 315- 330.

[15]

Weisburg WG , Barns SM , Pelletier DA , Lane DJ . 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991 Jan; 173 (2): 697- 703.

[16]

Schoch CL , Seifert KA , Huhndorf S , et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci USA. 2012 Apr 17; 109 (16): 6241- 6246.

[17]

Schloss PD , Handelsman J . Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl Environ Microbiol. 2005 Mar; 71 (3): 1501- 1506.

[18]

Callahan BJ , McMurdie PJ , Holmes SP . Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017 Dec 1; 11 (12): 2639- 2643.

[19]

Gilbert JA , Dupont CL . Microbial metagenomics: beyond the genome. Ann Rev Mar Sci. 2011 Jan 15; 3 (1): 347- 371.

[20]

Kang DD , Li F , Kirton E , et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019 Jul 26; 7: e7359.

[21]

Nissen JN , Johansen J , Allesøe RL , et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol. 2021 Mar; 39 (5): 555- 560.

[22]

Johansen J , Plichta DR , Nissen JN , et al. Genome binning of viral entities from bulk metagenomics data. Nat Commun. 2022 Feb 18; 13 (1): 965.

[23]

Langille MGI , Zaneveld J , Caporaso JG , et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013 Sep; 31 (9): 814- 821.

[24]

Xu Z , Malmer D , Langille MGI , Way SF , Knight R . Which is more important for classifying microbial communities: who's there or what they can do? ISME J. 2014 Dec 1; 8 (12): 2357- 2359.

[25]

Ning J , Beiko RG . Phylogenetic approaches to microbial community classification. Microbiome. 2015 Dec; 3 (1): 47.

[26]

Aitchison J . The statistical analysis of compositional data. J R Stat Soc Ser B Stat Methodol. 1982 Jan 1; 44 (2): 139- 160.

[27]

Quinn TP , Erb I , Richardson MF , Crowley TM . Understanding sequencing data as compositions: an outlook and review. In: Wren J, ed. Bioinformatics. vol. 34. 2018 Aug 15: 2870- 2878, 16.

[28]

Hu T , Gallins P , Zhou Y . A zero-inflated beta-binomial model for microbiome data analysis. Stat. 2018 Jan; 7 (1): e185.

[29]

Liu K , Bellet A . Escaping the curse of dimensionality in similarity learning: efficient frank-wolfe algorithm and generalization bounds [cited 2024 Jun 20]; Available from: https://arxiv.org/abs/1807.07789; 2018.

[30]

Mateu-Figueras G , Pawlowsky-Glahn V , Egozcue JJ . The principle of working on coordinates. In: Pawlowsky-Glahn V, Buccianti A, eds. Compositional Data Analysis [Internet]. 1st ed. Wiley. 2011:29–42. Accessed June 20, 2024;

[31]

Quinn TP . Visualizing balances of compositional data: a new alternative to balance dendrograms. F1000Research. 2018 Aug 14; 7: 1278.

[32]

Costea PI , Zeller G , Sunagawa S , Bork P . A fair comparison. Nat Methods. 2014 Apr; 11 (4): 359- 359.

[33]

Peng Hanchuan , Long Fuhui , Ding C . Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005 Aug; 27 (8): 1226- 1238.

[34]

Ditzler G , Morrison JC , Lan Y , Rosen GL . Fizzy: feature subset selection for metagenomics. BMC Bioinf. 2015 Dec; 16 (1): 358.

[35]

Weiss S , Xu ZZ , Peddada S , et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017 Dec; 5 (1): 27.

[36]

Tippanawar SA , Lad SS . A Textbook on Skin Diseases and Basic Pharmacology. 2014.

[37]

Deorankar PS , Vaidya VV , Munot NM , Jain KS , Patil AR . Optimizing healthcare throughput: the role of machine learning and data analytics. In: Biosystems, Biomedical & Drug Delivery Systems: Characterization. 2024.

[38]

Patil AR . Nutraceuticals and Ayurveda: the intersection of traditional medicine and modern science. In: Nanotechnology Applications in Medicinal Plants and Their Bionanocomposites. 2024.

[39]

Patvegar MTJ , Patil A , Jarag R , Sathe P , Patil A . Targets identification using machine learning: accelerating the hunt for potential drug targets. Int J. 2024; 7 (2): 488.

[40]

Thalange AV , Patil AR , Athavale VA . A review of artificial intelligence and machine learning for vaccine research. In: The International Conference on Recent Innovations in Computing. 2024; 85- 101.

[41]

Kamerikar AP , Phaphe RP , Pawar AM . Pharmacology for Nurses. vol. II. 2023.

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The Authors. Publishing services by Elsevier B.V. on behalf of Higher Education Press and KeAi Communications Co. Ltd.

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