Evidence map›Paper›PMID 42795682›Full record

ReviewMicroorganisms2026

The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.

Reza Nori, Parvin Shariati

Abstract readReview
PubMed Publisher
In one paragraph

Review in Microorganisms, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Reza NoriDepartment of Systems Biotechnology, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran P.O. Box 14965/161, Iran.
Parvin ShariatiDepartment of Bioprocess Engineering, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran P.O. Box 14965/161, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The microbiome, as a vast and dynamic community of microbes, is now recognized as a key regulator of host physiology, profoundly influencing health and susceptibility to disease. Accordingly, probiotics are a mainstay of prevention and treatment. But the individual complexity and dynamic specificity of an individual's microbiome make the previous "one-size-fits-all" research model completely invalid. This review systematically analyzes the applications of artificial intelligence (AI) and machine learning (ML) as transformative computational tools necessary to surmount these challenges. In this article, we detail how these computational methods have been applied throughout the entire research and development pathway of probiotics, including novel strain identification (through multi-omics analysis), formulation and production optimization, and elucidation of complex mechanisms of action in the host. Furthermore, we highlight the emerging frontier of personalized probiotic therapy, demonstrating how AI/ML can be utilized to predict treatment efficacy based on individual host data. The objective of this article is to provide a detailed discourse on the actual and prospective applications of AI and ML in this process, ultimately delineating their revolutionary potential to inform the design of the next generation of probiotics with unprecedented precision, efficacy, and sustainability.

Indexed as

artificial intelligencemachine learningmicrobiomepersonalized therapyprobioticsstrain identification

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.