Next Article in Journal
Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm
Previous Article in Journal
Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 14
Issue 19
10.3390/app14198627
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Perspective

Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art

by
Fabiana D’Urso
* and
Francesco Broccolo
*
Department of Experimental Medicine (DiMeS), University of Salento, 73100 Lecce, Italy
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2024, 14(19), 8627; https://doi.org/10.3390/app14198627
Submission received: 18 August 2024 / Revised: 10 September 2024 / Accepted: 19 September 2024 / Published: 25 September 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiota’s role in human health and disease, particularly focusing on CIDs and probiotic interventions.

1. Introduction

The gut microbiota is a complex and dynamic ecosystem that plays a fundamental role in human health and the pathogenesis of various diseases [1,2]. Composed of trillions of microorganisms, including bacteria, viruses, fungi, and protozoa, it constantly interacts with the host’s immune and metabolic systems, influencing vital processes such as digestion, vitamin production, and modulation of immune responses [3,4]. Alterations in the composition and functionality of the gut microbiota have been associated with a wide range of pathological conditions, including chronic inflammatory diseases (CIDs), metabolic disorders, autoimmune diseases, and even neurological disorders [5]. CIDs, often characterized by persistent low-grade inflammation without obvious symptoms, represent a significant global health burden [6]. These conditions, including inflammatory bowel diseases (IBDs), metabolic syndrome, and autoimmune disorders, have increasingly been linked to alterations in the composition and function of the gut microbiota [5,6]. Simultaneously, the use of probiotics as a therapeutic approach to modulate the gut microbiota has gained considerable attention [7]. Probiotic mixtures containing carefully selected bacterial strains offer the potential to restore microbial balance and mitigate inflammatory processes [8,9].
In recent years, AI has emerged as a powerful tool for analyzing and interpreting the diverse and complex information related to the microbiota [10]. Thanks to its ability to handle large amounts of data and identify hidden patterns, AI offers new opportunities to better understand the interactions between the gut microbiota and human health [11].
This review aims to explore how AI can be used to unravel the functionalities of the gut microbiota and its impact on CIDs. Recent studies that used AI techniques to analyze microbiota data, identify disease biomarkers, and predict therapeutic responses are examined [10,12]. Furthermore, probiotic interventions, which aim to modulate the gut microbiota to improve host health, are discussed. AI can play a crucial role in optimizing these interventions by helping to identify the most effective probiotic combinations and personalize therapies based on individual microbiota profiles.

2. AI-Driven Analysis of Microbiota Functionality

2.1. The Impact of AI on Microbial Analysis

AI has revolutionized microbial diagnosis, offering unprecedented speed, accuracy, and depth of analysis. Traditional methods of microbial identification and characterization often require time-consuming culture-based techniques or complex molecular biology procedures. AI-driven approaches are transforming this landscape by enabling rapid, culture-free, and highly sensitive diagnostic tools. One of the most significant advancements is the application of machine learning algorithms to analyze high-throughput sequencing data for pathogen detection. For instance, Oh et al. developed a deep learning model called DeepMicro, which can accurately identify bacterial species from metagenomic sequencing data with high sensitivity and specificity [12]. This approach allows for the simultaneous detection of multiple pathogens, including those that are difficult to culture or present in low abundance. AI is also enhancing the interpretation of antimicrobial susceptibility testing results.

2.2. Machine Learning (ML) and Deep Learning Approaches for Microbiome Data Analysis

The rapid growth in computing power alongside innovative algorithms has led to the creation and analysis of vast amounts of data at speeds previously unimaginable [13]. ML plays a critical role in this process, utilizing advanced algorithms to learn from existing data and reveal complex patterns and relationships that are often missed by traditional analytical methods [14].
ML is particularly valuable in studying the human gut microbiota, where it provides insights into this complex microbial community’s influence on health and disease. Even when dealing with high-dimensional, heterogeneous data, ML techniques like feature selection, biomarker identification, disease prediction, and treatment recommendation can be used to analyze and interpret the data effectively [15].
The application of ML algorithms to microbiome data has revolutionized our ability to interpret complex microbial communities. Supervised learning methods, such as random forests and support vector machines, have been successfully employed to classify microbiome samples and predict host phenotypes [16]. For instance, Pasolli et al. used random forest classifiers to predict host characteristics from gut microbiome data with high accuracy [17].
Unsupervised learning techniques, including clustering algorithms and dimensionality reduction methods, have proven valuable in identifying microbial community structures and functional modules. A study by Knights et al. utilized k-means clustering to identify distinct enterotypes in the human gut microbiome [18].
Deep learning approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in analyzing time-series microbiome data and predicting microbial interactions [19]. For example, Fioravanti et al. developed a CNN-based model to predict antibiotic resistance from metagenomic data with high accuracy [20]. The integration of AI with large-scale metagenomic databases has greatly enhanced our understanding of microbiota functionality. Machine learning algorithms can efficiently mine these databases to identify functional genes, metabolic pathways, and potential biomarkers [21].
One notable example is the application of graph neural networks (GNNs) to analyze microbial interaction networks. Zhu et al. developed a GNN-based model that accurately predicts microbial co-occurrence patterns and functional relationships from metagenomic data [22].
The application of ML in microbiome research typically involves a process of data splitting, feature engineering and selection, hyperparameter tuning, and validation. These steps ensure that models are generalizable and capable of making accurate predictions. For example, in a case-control study, ML models can identify key features that differentiate between healthy and diseased states by analyzing high-throughput sequencing data, such as whole-genome sequencing (WGS) or 16S rRNA sequencing. This approach offers a powerful alternative to traditional statistical methods, providing deeper insights into microbiome dynamics and their association with diseases. High-throughput sequencing data are particularly useful in microbiome research because they provide a detailed snapshot of microbial diversity. AI models like DeepMicro can process these huge datasets to detect patterns, classify microbes, and predict disease associations. Without AI, it would be extremely difficult to analyze the large volumes of data produced by high-throughput sequencing.
Deep learning is a subset of ML, which is itself a branch of AI. Deep learning models use multiple layers of artificial neurons, similar to how the human brain works, to process and analyze complex data. These models are particularly useful when dealing with unstructured data like images, text, or, in this case, microbiome sequencing data. At the core of deep learning are neural networks. These are computational models made up of interconnected layers of artificial neurons. Each neuron takes input, performs a computation (usually a mathematical function), and produces an output, which is passed to the next layer of neurons.
A deep learning model, such as DeepMicro, can be trained on microbiome sequencing data. After training, it can predict whether a specific microbial composition is associated with conditions like IBD or other CIDs. The model can learn patterns that link the presence or absence of certain microbial species to health outcomes, making it a powerful tool in precision medicine [12].
Gut microbiome research leverages advanced methodologies like sequencing, metabolomics, and transcriptomics, which generate highly complex and diverse datasets. The vast scale of these data requires the precision and flexibility of ML for effective analysis. Omics techniques such as meta-transcriptomics, metabolomics, and metagenomics are frequently used to study the human gut microbiota. These techniques enable a comprehensive and detailed examination of the entire microbial community, providing multifaceted insights into its structure and function. For instance, metagenomics often employs whole-genome shotgun sequencing to analyze the genetic composition of the microbiome, while metabolomics focuses on quantifying the metabolites produced by the microbial community. The intricate and voluminous nature of data from these omics methods necessitates advanced computational tools, with ML playing a crucial role in integrating and interpreting these datasets effectively (Figure 1). Below is an explanation of how AI can be integrated into the diagnosis and understanding of microbiome analysis, along with examples related to metabolomics, transcriptomics, proteomics, and genomics (Table 1).

3. Dysbiosis and Clinical Implications

Dysbiosis, an imbalance in the gut microbial community, has been associated with various pathological conditions. This state is characterized by a loss of microbial diversity, changes in the relative abundances of different taxa, and alterations in microbial functionality [16]. Several factors can contribute to dysbiosis. Western-style diets, for example, rich in fats and sugars and low in fiber, have been shown to rapidly alter the composition of the gut microbiome [17]. While life-saving in many situations, antibiotics can cause significant disruptions in the gut microbiota, potentially leading to long-term alterations [18]. Both acute and chronic stress can also influence the composition and function of the gut microbiota through neuroendocrine pathways [19]. Dysbiosis has been implicated in a wide range of clinical conditions, including many IBDs. Both Crohn’s disease and ulcerative colitis are associated with significant alterations in the gut microbiota, including reduced diversity and increased potentially pathogenic species [23,24]. Obesity, type 2 diabetes, and non-alcoholic fatty liver disease have been linked to specific changes in the composition and function of the gut microbiome [25]. Similarly, rheumatoid arthritis, multiple sclerosis, and systemic lupus erythematosus have been associated with alterations in the gut microbiota [26]. Emerging evidence suggests a role for the gut microbiota in conditions such as autism spectrum disorders, depression, and Parkinson’s disease [27]. Understanding the complex interaction between the gut microbiota and human health is crucial for developing targeted interventions to prevent and treat these conditions. In this context, the use of ML algorithms can make a significant difference, starting from the multitude of specific data related to the microbiota system.

3.1. Clinical Evidence for Probiotic Mixtures in CIDs

Several studies have demonstrated the efficacy of multi-strain probiotics in various CIDs. A randomized controlled trial by Tursi et al. showed that a mixture of eight probiotic strains (VSL#3) was effective in maintaining remission in ulcerative colitis patients [28]. Sabico et al. reported that a four-strain probiotic combination improved glycemic control and reduced systemic inflammation in individuals with type 2 diabetes [29]. A study by Zamani et al. found that a probiotic mixture containing seven strains improved disease activity and inflammatory markers in rheumatoid arthritis patients [30]. Tamtaji et al. demonstrated that a three-strain probiotic mixture improved cognitive function and metabolic status in Alzheimer’s disease patients [31]. The development of AI-driven algorithms that integrate clinical data with microbiome profiles shows promise in optimizing probiotic interventions (Figure 2).
ML algorithms can be used to predict the most effective probiotic combinations, based on an individual’s microbiome profile and clinical characteristics [32]. Continuous monitoring of microbiome changes in response to probiotic interventions can allow for real-time adjustments to treatment strategies [33]. Combining microbiome data with other omics data (e.g., metabolomics, proteomics) can provide a more comprehensive understanding of the impact of probiotic interventions [34]. Despite the promising advancements, several challenges remain in the application of AI to microbiome research and antibiotic resistance. These include the following:
(a)
Data quality and standardization: Ensuring consistent and high-quality data across different studies and platforms is crucial for developing robust AI models [27].
(b)
Interpretability: Many AI models, particularly deep learning approaches, operate as “black boxes,” making it challenging to interpret their decision-making processes [35].
(c)
Integration of multi-omics data: Developing AI models that can effectively integrate diverse data types (e.g., metagenomics, metabolomics, and host genomics) remains a significant challenge [36].
(d)
Ethical considerations: The use of AI in healthcare raises important ethical questions regarding data privacy, algorithmic bias, and clinical decision-making [37]. Future research directions should focus on addressing these challenges and exploring new AI paradigms. The development of explainable AI models, federated learning approaches for privacy-preserving analysis, and the integration of AI with other emerging technologies (e.g., single-cell sequencing and microfluidics) hold great promise for advancing our understanding of the microbiome and combating antibiotic resistance.

3.2. Microbiome Profiling and Targeted Interventions

Advances in microbiome sequencing and analysis allow for personalized approaches to probiotic interventions. High-resolution microbiome profiling can identify specific bacterial strains that are depleted or overabundant in an individual’s gut [32]. Metagenomic and metabolomic analyses can reveal functional deficits in the microbiome that could be addressed by specific probiotic strains [33]. Furthermore, integrating host genetic and immune data with microbiome profiles can help predict individual responses to probiotic interventions [34]. The development of AI-driven algorithms that integrate clinical data with microbiome profiles shows promising possibilities for optimizing probiotic interventions. Machine learning algorithms can be used to predict the most effective probiotic combinations based on an individual’s microbiome profile and clinical characteristics [27]. Continuous monitoring of microbiome changes in response to probiotic interventions can allow for real-time adjustments to treatment strategies [38]. Despite the promising potential of probiotic mixtures, several points remain to be analyzed. First and foremost, there is a need for standardized protocols for the production, formulation, and quality control of probiotics [32]. Additionally, more research is needed to elucidate the precise mechanisms by which probiotic mixtures exert their effects [33]. The development of truly personalized probiotic interventions requires more sophisticated algorithms and larger datasets [36].
By leveraging AI, researchers and clinicians can gain deeper insights into microbiome dynamics and optimize probiotic treatments, ultimately leading to more personalized and effective healthcare solutions. AI can be used to understand probiotic mixtures in different clinical settings (Table 2).

4. Current and Potential Solutions to Improve Microbiome Analysis and Probiotic Interventions

The results of this study highlight the potential of AI in microbiome analysis and probiotic interventions. The integration of ML algorithms with microbiome data has enabled a deeper understanding of the interactions between the gut microbiota and human health. These findings are consistent with existing knowledge that emphasizes the importance of the microbiome in host physiology and associated pathologies. The theoretical implications of this study are significant. The use of AI for microbiome profiling and the personalization of probiotic interventions represents a step forward in precision medicine. The ability to identify specific bacterial strains and predict individual responses to probiotic interventions could revolutionize the way we treat a wide range of pathological conditions. From a practical standpoint, the results of this study suggest that integrating AI into microbiome research could lead to more effective and personalized therapies. However, it is important to acknowledge the limitations of the study. The standardization of protocols for the production, formulation, and quality control of probiotics remains a challenge. Additionally, the understanding of the mechanisms through which probiotics exert their effects is still incomplete. For further research, it would be useful to deepen the analysis of the mechanisms of action of probiotics and develop more sophisticated algorithms for the personalization of interventions. Moreover, the collection of larger and more diverse datasets could improve the accuracy of AI-based predictions. Finally, ethical and regulatory considerations must be carefully evaluated to ensure that the use of AI in microbiome research is safe, fair, and transparent. This study demonstrates the potential of AI in microbiome analysis and probiotic interventions, paving the way for new therapeutic strategies that could significantly improve human health.

5. Conclusions and Future Perspective

The integration of AI into microbiome research represents a significant breakthrough in the field of biomedicine. Machine learning techniques and big data analysis have enabled an unprecedented understanding of the complex interactions between the gut microbiota and human health. Through high-resolution microbiome profiling, it is possible to identify specific bacterial strains that play crucial roles in human host physiology and associated pathologies. Additionally, AI allows for detailed functional profiling, revealing metabolic deficits that can be corrected through targeted probiotic interventions.
High-throughput sequencing generates vast amounts of microbiome data, capturing the diversity and complexity of microbial communities. Deep learning models like DeepMicro are powerful AI tools that can analyze this data, extract patterns, and make predictions about health outcomes, such as identifying potential disease markers or predicting the success of probiotic interventions. These technologies together enable a much deeper understanding of the microbiome and its impact on human health, providing the foundation for the development of AI-driven solutions in microbiome-based therapies.
AI-driven algorithms also offer the possibility of personalizing probiotic interventions by predicting individual responses based on host genetic and immune profiles. This personalized approach promises to significantly improve the efficacy of probiotic therapies while reducing side effects and optimizing clinical outcomes. However, to fully realize the potential of these technologies, several challenges must be addressed. Firstly, the standardization of protocols for the production, formulation, and quality control of probiotics is essential to ensure the consistency and reliability of the results. Furthermore, it is crucial to deepen the understanding of the mechanisms through which probiotics exert their effects to develop more targeted and effective interventions. Personalizing probiotic interventions also requires more sophisticated algorithms and larger datasets that can capture the complexity of host–microbiota interactions. Finally, ethical and regulatory considerations must be carefully evaluated to ensure that the use of AI in microbiome research is safe, fair, and transparent. Despite these challenges, the integration of AI with other cutting-edge technologies promises to revolutionize our understanding of microbial communities and pave the way for personalized, microbiome-based therapies. With further research and interdisciplinary collaborations, we can hope to develop innovative therapeutic strategies that improve human health and effectively address a wide range of pathological conditions.

Author Contributions

Conceptualization, F.B.; methodology, F.D.; writing—original draft preparation, F.D.; writing—review and editing, F.D. and F.B.; supervision, F.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to thank Francesca Di Gaudio for her technical support and suggestions for drafting the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Turnbaugh, P.J.; Ley, R.E.; Mahowald, M.A.; Magrini, V.; Mardis, E.R.; Gordon, J.I. The human microbiome project. Nature 2007, 449, 804–810. [Google Scholar] [CrossRef] [PubMed]
  2. Ursell, L.K.; Metcalf, J.L.; Parfrey, L.W.; Knight, R. Defining the human microbiome. Nutr. Rev. 2012, 70 (Suppl. 1), S38–S44. [Google Scholar] [CrossRef] [PubMed]
  3. Dekaboruah, E.; Suryavanshi, M.V.; Chettri, D.; Verma, A.K. Human microbiome: An academic update on human body site specific surveillance and its possible role. Arch. Microbiol. 2020, 202, 2147–2167. [Google Scholar] [CrossRef] [PubMed]
  4. Sender, R.; Fuchs, S.; Milo, R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 2016, 14, e1002533. [Google Scholar] [CrossRef]
  5. Lynch, S.V.; Pedersen, O. The Human Intestinal Microbiome in Health and Disease. N. Engl. J. Med. 2016, 375, 2369–2379. [Google Scholar] [CrossRef] [PubMed]
  6. Belkaid, Y.; Hand, T.W. Role of the microbiota in immunity and inflammation. Cell 2014, 157, 121–141. [Google Scholar] [CrossRef]
  7. Hoffman, D.E.; Fraser, C.M.; Palumbo, F.B.; Ravel, J.; Rothenberg, K.; Rowthorn, V.; Schwartz, J. Probiotics: Finding the right regulatory balance. Science 2013, 342, 314–315. [Google Scholar] [CrossRef]
  8. Suez, J.; Zmora, N.; Segal, E.; Elinav, E. The pros, cons, and many unknowns of probiotics. Nat. Med. 2019, 25, 716–729. [Google Scholar] [CrossRef]
  9. O’Toole, P.W.; Marchesi, J.R.; Hill, C. Next-generation probiotics: The spectrum from probiotics to live biotherapeutics. Nat. Microbiol. 2017, 2, 17057. [Google Scholar] [CrossRef]
  10. Abavisani, M.; Khoshrou, A.; Foroushan, S.K.; Ebadpour, N.; Sahebkar, A. Deciphering the gut microbiome: The revolution of artificial intelligence in microbiota analysis and intervention. Curr. Res. Biotechnol. 2024, 7, 100211. [Google Scholar] [CrossRef]
  11. Zhang, C.; Wang, J.; Li, J.; Liu, X.; Wang, X. Artificial Intelligence in Microbiome Research: Opportunities and Challenges. Front. Microbiol. 2020, 11, 527458. [Google Scholar]
  12. Oh, M.; Zhang, L. DeepMicro: Deep representation learning for disease prediction based on microbiome data. Sci. Rep. 2020, 10, 6026. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, L.; Zhang, L. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities. IEEE Geosci. Remote Sens. 2022, 10, 270–294. [Google Scholar] [CrossRef]
  14. Kumar, S.; Sharma, D.; Rao, S.; Lim, W.M.; Mangla, S.K. Past, present, and future of sustainable finance: Insights from big data analytics through machine learning of scholarly research. Ann. Oper. Res. 2022, 1–44. [Google Scholar] [CrossRef]
  15. Heshiki, Y.; Vazquez-Uribe, R.; Li, J.; Ni, Y.; Quainoo, S.; Imamovic, L.; Li, J.; Sørensen, M.; Chow, B.K.C.; Weiss, G.J.; et al. Predictable modulation of cancer treatment outcomes by the gut microbiota. Microbiome 2020, 8, 28. [Google Scholar] [CrossRef] [PubMed]
  16. Knights, D.; Costello, E.K.; Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 2011, 35, 343–359. [Google Scholar] [CrossRef]
  17. Pasolli, E.; Truong, D.T.; Malik, F.; Waldron, L.; Segata, N. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLoS Comput. Biol. 2016, 12, e1004977. [Google Scholar] [CrossRef]
  18. Knights, D.; Ward, T.L.; McKinlay, C.E.; Miller, H.; Gonzalez, A.; McDonald, D.; Knight, R. Rethinking “enterotypes”. Cell Host Microbe 2014, 16, 433–437. [Google Scholar] [CrossRef]
  19. Mallick, H.; Franzosa, E.A.; Mclver, L.J.; Banerjee, S.; Sirota-Madi, A.; Kostic, A.D.; Clish, C.B.; Vlamakis, H.; Xavier, R.J.; Huttenhower, C. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 2019, 10, 3136. [Google Scholar] [CrossRef]
  20. Fioravanti, A.; Fustaino, V.; Orsini, M.; Corsi, C.; Arrigo, P.; Patarnello, T.; Carbonneau, M.A.; Mauri, M.; Biondi, E.G. Phylogenetic convolutional neural networks in metagenomics. BMC Bioinform. 2018, 19, 49. [Google Scholar] [CrossRef]
  21. Marcos-Zambrano, L.J.; Karaduzovic-Hadziabdic, K.; Loncar Turukalo, T.; Przymus, P.; Trajkovik, V.; Aasmets, O.; Berland, M.; Gruca, A.; Hasic, J.; Hron, K.; et al. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front. Microbiol. 2021, 12, 634511. [Google Scholar] [CrossRef]
  22. Zhu, Q.; Pan, M.; Liu, J.; Wang, H.; Wei, Y.; Liu, Y.; Xu, Z.; Yang, S.; Wei, H.; Huang, Y.; et al. MicrobioGraph: A Graph Neural Network-Based Framework for Inferring Microbial Interactions from Metagenomic Data. bioRxiv 2021. bioRxiv:2021.02.11.430831. [Google Scholar]
  23. Arango-Argoty, G.; Garner, E.; Pruden, A.; Heath, L.S.; Vikesland, P.; Zhang, L. DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 2018, 6, 23. [Google Scholar] [CrossRef] [PubMed]
  24. Stokes, J.M.; Yang, K.; Swanson, K.; Jin, W.; Cubillos-Ruiz, A.; Donghia, N.M.; MacNair, C.R.; French, S.; Carfrae, L.A.; Bloom-Ackermann, Z.; et al. A Deep Learning Approach to Antibiotic Discovery. Cell 2020, 180, 688–702.e13. [Google Scholar] [CrossRef]
  25. Yang, K.K.; Wu, Z.; Bedbrook, C.N.; Arnold, F.H. Learned protein embeddings for machine learning. Bioinformatics 2018, 34, 2642–2648. [Google Scholar] [CrossRef] [PubMed]
  26. Yelin, I.; Snitser, O.; Novich, G.; Katz, R.; Tal, O.; Parizade, M.; Chodick, G.; Koren, G.; Shalev, V.; Kishony, R. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat. Med. 2019, 25, 1143–1152. [Google Scholar] [CrossRef] [PubMed]
  27. Sinha, R.; Abnet, C.C.; White, O.; Knight, R.; Huttenhower, C. The microbiome quality control project: Baseline study design and future directions. Genome Biol. 2015, 16, 276. [Google Scholar] [CrossRef]
  28. Tursi, A.; Brandimarte, G.; Papa, A.; Giglio, A.; Elisei, W.; Giorgetti, G.M.; Forti, G.; Morini, S.; Hassan, C.; Pistoia, M.; et al. Treatment of relapsing mild-to-moderate ulcerative colitis with the probiotic VSL#3 as adjunctive to a standard pharmaceutical treatment: A double-blind, randomized, placebo-controlled study. Am. J. Gastroenterol. 2010, 105, 2218–2227. [Google Scholar]
  29. Sabico, S.; Al-Mashharawi, A.; Al-Daghri, N.M.; Yakout, S.; Alnaami, A.M.; Alokail, M.S.; McTernan, P.G. Effects of a multi-strain probiotic supplement for 12 weeks in circulating endotoxin levels and cardiometabolic profiles of medication naïve T2DM patients: A randomized clinical trial. J. Transl. Med. 2019, 17, 286. [Google Scholar] [CrossRef]
  30. Zamani, B.; Golkar, H.R.; Farshbaf, S.; Emadi-Baygi, M.; Tajabadi-Ebrahimi, M.; Jafari, P.; Akhavan, R.; Taghizadeh, M.; Memarzadeh, M.R.; Asemi, Z. Clinical and metabolic response to probiotic administration in patients with rheumatoid arthritis: A randomized, double-blind, placebo-controlled trial. Int. J. Rheum. Dis. 2016, 19, 869–879. [Google Scholar] [CrossRef]
  31. Tamtaji, O.R.; Taghizadeh, M.; Daneshvar Kakhaki, R.; Kouchaki, E.; Bahmani, F.; Borzabadi, S.; Oryan, S.; Mafi, A.; Asemi, Z. Clinical and metabolic response to probiotic administration in people with Parkinson’s disease: A randomized, double-blind, placebo-controlled trial. Clin. Nutr. 2019, 38, 1031–1035. [Google Scholar] [CrossRef]
  32. Zeevi, D.; Korem, T.; Zmora, N.; Israeli, D.; Rothschild, D.; Weinberger, A.; Ben-Yacov, O.; Lador, D.; Avnit-Sagi, T.; Lotan-Pompan, M. Personalized Nutrition by Prediction of Glycemic Responses. Cell 2015, 163, 1079–1094. [Google Scholar] [CrossRef] [PubMed]
  33. Lloyd-Price, J.; Arze, C.; Ananthakrishnan, A.N.; Schirmer, M.; Avila-Pacheco, J.; Poon, T.W.; Andrews, E.; Ajami, N.J.; Bonham, K.S.; Brislawn, C.J.; et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 2019, 569, 655–662. [Google Scholar] [CrossRef] [PubMed]
  34. Rothschild, D.; Weissbrod, O.; Barkan, E.; Kurilshikov, A.; Korem, T.; Zeevi, D.; Costea, P.I.; Godneva, A.; Kalka, I.N.; Bar, N. Environment dominates over host genetics in shaping human gut microbiota. Nature 2018, 555, 210–215. [Google Scholar] [CrossRef]
  35. Lipton, Z.C. The Mythos of Model Interpretability. Queue 2018, 16, 31–57. [Google Scholar] [CrossRef]
  36. Heintz-Buschart, A.; Wilmes, P. Human Gut Microbiome: Function Matters. Trends Microbiol. 2018, 26, 563–574. [Google Scholar] [CrossRef]
  37. Char, D.S.; Shah, N.H.; Magnus, D. Implementing Machine Learning in Health Care—Addressing Ethical Challenges. N. Engl. J. Med. 2018, 378, 981–983. [Google Scholar] [CrossRef]
  38. Franzosa, E.A.; McIver, L.J.; Rahnavard, G.; Thompson, L.R.; Schirmer, M.; Weingart, G.; Lipson, K.S.; Knight, R.; Caporaso, J.G.; Segata, N.; et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 2018, 15, 962–968. [Google Scholar] [CrossRef]
Applsci 14 08627 g001
Figure 1. Integration of AI in microbiome analysis. NOTA. This diagram represents how AI integrates into different areas of microbiome analysis, illustrating the flow from data analysis to actionable insights.
Figure 1. Integration of AI in microbiome analysis. NOTA. This diagram represents how AI integrates into different areas of microbiome analysis, illustrating the flow from data analysis to actionable insights.
Applsci 14 08627 g001
Applsci 14 08627 g002
Figure 2. AI in probiotic mixtures and clinical settings. NOTA. This diagram represents how AI integrates into different probiotic applications, illustrating the flow from data analysis to actionable insights.
Figure 2. AI in probiotic mixtures and clinical settings. NOTA. This diagram represents how AI integrates into different probiotic applications, illustrating the flow from data analysis to actionable insights.
Applsci 14 08627 g002
Table 1. Integration of AI in microbiome analysis.
Table 1. Integration of AI in microbiome analysis.
Microbiome
Analysis		ExplanationAI ApplicationExample
1. MetabolomicsMetabolomics involves the comprehensive analysis of metabolites in a biological system. These metabolites can provide insights into metabolic changes and microbial interactions.AI algorithms, particularly machine learning models, can analyze complex metabolomic data to identify patterns and correlations between metabolites and microbial communities. For instance, AI can use clustering techniques to categorize different metabolic profiles associated with specific microbiome compositions or health conditions.
A study might use AI to analyze urine and blood samples from patients with metabolic disorders to identify specific metabolite patterns linked to gut microbiome composition. This could help in identifying biomarkers for early diagnosis or personalized treatment plans.
2. TranscriptomicsTranscriptomics focuses on the RNA transcripts produced by the genome under specific circumstances. It helps in understanding gene expression patterns within the microbiome.AI can be used to process and interpret large-scale transcriptomic data to discern gene expression changes in microbial communities. For example, deep learning models can analyze RNA sequencing data to identify differentially expressed genes that are influenced by or influence the microbiome.AI-driven tools can analyze transcriptomic data from gut microbiome samples to identify how microbial gene expression changes in response to dietary interventions, helping to understand which microbial genes are linked to improved health outcomes.
3. ProteomicsProteomics involves studying the entire set of proteins produced by an organism. It provides insights into the functional aspects of the microbiome.AI can facilitate the analysis of proteomic data by identifying protein expression patterns and interactions within the microbiome. Machine learning algorithms can predict the functional impact of specific proteins or protein interactions.AI models can be used to analyze protein expression profiles in the gut microbiome to identify proteins that correlate with CIDs, potentially leading to the discovery of new therapeutic targets.
4. GenomicsGenomics involves studying the complete DNA sequence of an organism, including the microbiome. It helps in understanding genetic variations and their impact on microbial function. Genomics focuses on the genome of an individual organism, while metagenomics studies the combined genetic material from an entire community of organisms. Similarly, microbiota refers to the organisms themselves, while microbiome emphasizes the genes and functional capacities of these organisms.AI can assist in analyzing genomic data to map microbial genomes, identify genetic variations, and predict their functional consequences. For instance, AI models can analyze metagenomic data to link specific microbial genes with health outcomes.AI-driven genomic analysis can identify specific microbial genes associated with antibiotic resistance. This information can be used to predict resistance patterns and inform treatment strategies.
Table 2. AI in probiotic mixtures and clinical settings.
Table 2. AI in probiotic mixtures and clinical settings.
ExplanationApplicationExample
1. Predictive ModelingAI can predict how different probiotic strains will interact with the microbiome and influence health outcomes, based on historical data and clinical trials.By analyzing data from previous studies and clinical trials, AI can identify which probiotic strains are most effective for specific conditions or patient demographics.An AI model might predict that a certain combination of Lactobacillus and Bifidobacterium strains is particularly effective for managing irritable bowel syndrome (IBS) in adults, based on a comprehensive analysis of patient data and clinical outcomes.
2. Personalized Probiotic RecommendationsAI can be used to tailor probiotic recommendations based on individual microbiome profiles and health conditions.By integrating data from genomic, metabolomic, and clinical sources, AI can recommend personalized probiotic mixtures that are more likely to be effective for individual patientsFor a patient with a disrupted gut microbiome and specific metabolic abnormalities, an AI system might suggest a custom probiotic blend that targets the imbalances observed in their microbiome.
3. Clinical Decision SupportAI can support clinicians in selecting appropriate probiotic treatments by analyzing patient data and predicting potential outcomes.AI systems can provide evidence-based recommendations for probiotic use in various clinical settings, such as managing chronic diseases or supporting recovery after antibiotic treatment.In a hospital setting, AI could analyze data from patients undergoing antibiotic therapy to recommend specific probiotics that could help prevent antibiotic-associated diarrhea.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

D’Urso, F.; Broccolo, F. Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art. Appl. Sci. 2024, 14, 8627. https://doi.org/10.3390/app14198627

AMA Style

D’Urso F, Broccolo F. Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art. Applied Sciences. 2024; 14(19):8627. https://doi.org/10.3390/app14198627

Chicago/Turabian Style

D’Urso, Fabiana, and Francesco Broccolo. 2024. "Applications of Artificial Intelligence in Microbiome Analysis and Probiotic Interventions—An Overview and Perspective Based on the Current State of the Art" Applied Sciences 14, no. 19: 8627. https://doi.org/10.3390/app14198627

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop