Machine Learning in Nutrition Research

Advances in Nutrition, Journal Year: 2022, Volume and Issue: 13(6), P. 2573 - 2589

Published: Sept. 27, 2022

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods data analysis. The characteristics machine learning (ML) make it suitable for such analysis thus lend itself as an alternative tool to deal this nature. ML has already been applied important problem areas nutrition, obesity, metabolic health, malnutrition. Despite this, experts often without understanding ML, which limits its application therefore potential solve open questions. current article aims bridge knowledge gap by supplying researchers a resource facilitate use their research. is first explained distinguished from existing solutions, key examples applications literature provided. Two case studies domains particularly applicable, precision metabolomics, then presented. Finally, framework outlined guide interested integrating into work. By acting can refer, we hope support integration modern

Language: Английский

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Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Diagnostics, Journal Year: 2024, Volume and Issue: 14(5), P. 484 - 484

Published: Feb. 23, 2024

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents foundation for effective prevention control of HAIs, yet conventional surveillance is costly labor intensive. Artificial intelligence (AI) machine learning (ML) have potential to support development HAI algorithms understanding risk factors, improvement patient stratification as well prediction timely detection infections. AI-supported systems so far been explored clinical laboratory testing imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery prediction-based decision tools terms HAIs. This review aims provide comprehensive summary current literature on AI applications field HAIs discuss future potentials this emerging technology infection practice. Following PRISMA guidelines, study examined articles databases including PubMed Scopus until November 2023, which were screened based inclusion exclusion criteria, resulting 162 included articles. By elucidating advancements field, we aim highlight report related issues shortcomings directions.

Language: Английский

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Gut microbiota in Alzheimer’s disease: Understanding molecular pathways and potential therapeutic perspectives

Ageing Research Reviews, Journal Year: 2025, Volume and Issue: 104, P. 102659 - 102659

Published: Jan. 10, 2025

Language: Английский

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Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data

Ecotoxicology and Environmental Safety, Journal Year: 2025, Volume and Issue: 291, P. 117609 - 117609

Published: Feb. 1, 2025

Groundwater pollution, particularly in retired pesticide sites, is a significant environmental concern due to the presence of chlorinated aliphatic hydrocarbons (CAHs) and benzene, toluene, ethylbenzene, xylene (BTEX). These contaminants pose serious risks ecosystems human health. Natural attenuation (NA) has emerged as sustainable solution, with microorganisms playing crucial role pollutant biodegradation. However, interpretation diverse microbial communities relation complex pollutants still challenging, there limited research multi-polluted groundwater. Advanced machine learning (ML) algorithms help identify key indicators for different pollution types (CAHs, BTEX plumes, mixed plumes). The accuracy Area Under Curve (AUC) achieved by Support Vector Machines (SVM) were impressive, values 0.87 0.99, respectively. With assistance model explanation methods, we identified bioindicators which then analyzed using co-occurrence network analysis better understand their potential roles degradation. genera indicate that oxidation co-metabolism predominantly drive dechlorination processes within CAHs group. In group, primary mechanism degradation was observed be anaerobic under sulfate-reducing conditions. CAHs&BTEX groups, indicative suggested occurred iron-reducing conditions reductive existed. Overall, this study establishes framework harnessing power ML alongside based on microbiome data enhance understanding provide robust assessment natural process at sites.

Language: Английский

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Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Frontiers in Microbiology, Journal Year: 2021, Volume and Issue: 12

Published: Feb. 22, 2021

The human microbiome has emerged as a central research topic in biology and biomedicine. Current studies generate high-throughput omics data across different body sites, populations, life stages. Many of the challenges are similar to other studies, quantitative analyses need address heterogeneity data, specific statistical properties, remarkable variation composition individuals sites. This led broad spectrum machine learning that range from study design, processing, standardization analysis, modeling, cross-study comparison, prediction, science ecosystems, reproducible reporting. Nevertheless, although many statistics approaches tools have been developed, new techniques needed deal with emerging applications vast data. We review discuss introduce COST Action CA18131 "ML4Microbiome" brings together researchers experts current such analysis pipelines for reproducibility results, benchmarking, improvement, or development existing ontologies.

Language: Английский

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Harnessing machine learning for development of microbiome therapeutics

Gut Microbes, Journal Year: 2021, Volume and Issue: 13(1)

Published: Jan. 1, 2021

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the between an unbalanced and disease are currently published daily. As such, big data have become a reality that provide mine information for development new therapeutics. Machine learning (ML), branch artificial intelligence, offers powerful techniques analysis prediction-making, out reach intellect alone. This review will explore how ML can be applied microbiome-targeted A background on given, followed by guide where to find reliable data. Existing applications opportunities discussed, including use discover, design, characterize optimize advanced processes, such as 3D printing in silico prediction drug-microbiome interactions, also highlighted. Finally, barriers adoption academic industrial settings examined, concluded future outlook field.

Language: Английский

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A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression

Nutrients, Journal Year: 2023, Volume and Issue: 15(6), P. 1382 - 1382

Published: March 13, 2023

Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents key domain healthcare, which has limited, adverse-effect prone treatment options, probiotics may hold be novel, customizable for depression. Clinical depression is common, potentially debilitating condition that amenable precision psychiatry-based approach utilizing probiotics. Although our understanding not yet reached sufficient level, this could therapeutic can tailored specific individuals with own unique set characteristics issues. Scientifically, use as valid basis rooted in microbiota-gut-brain axis (MGBA) mechanisms, play role pathophysiology theory, appear ideal adjunct therapeutics major depressive disorder (MDD) stand-alone mild MDD revolutionize disorders. there wide range an almost limitless combinations, review aims narrow focus most widely commercialized studied strains, namely Lactobacillus Bifidobacterium, bring together arguments usage patients (MDD). Clinicians, scientists, industrialists critical stakeholders exploring groundbreaking concept.

Language: Английский

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Exploring the Influence of Gut Microbiome on Energy Metabolism in Humans

Advances in Nutrition, Journal Year: 2023, Volume and Issue: 14(4), P. 840 - 857

Published: April 7, 2023

The gut microbiome has a profound influence on host physiology, including energy metabolism, which is the process by from nutrients transformed into other forms of to be used body. However, mechanistic evidence for how influences metabolism derived animal models. In this narrative review, we included human studies investigating relationship between and -i.e., expenditure in humans harvest microbiome. Studies have found no consistent patterns associated with most interventions were not effective modulating metabolism. To date, cause-and-effect relationships impact been established humans. Future longitudinal observational randomized controlled trials utilizing robust methodologies advanced statistical analysis are needed. Such knowledge would potentially inform design therapeutic avenues specific dietary recommendations improve through modulation.

Language: Английский

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Artificial intelligence in soil microbiome analysis: a potential application in predicting and enhancing soil health—a review

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(2)

Published: Jan. 16, 2025

Abstract Soil is a depletable and non-renewable resource essential for food production, crop growth, supporting ecosystem services, such as the retaining cycling of various elements, including water. Therefore characterization preservation soil biological health key point development sustainable agriculture. We conducted comprehensive review use Artificial Intelligence (AI) techniques to develop forecasting models based on microbiota data able monitor predict health. also investigated potentiality AI-based Decision Support Systems (DSSs) improving microorganisms enhance fertility. While available studies are limited, potential applications AI seem relevant predictive fertility, its properties activities, implement precision agriculture, safeguarding ecosystems, bolstering resilience, ensuring production high-quality food.

Language: Английский

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New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

Journal of Fungi, Journal Year: 2022, Volume and Issue: 8(7), P. 737 - 737

Published: July 16, 2022

The fast and continued progress of high-throughput sequencing (HTS) the drastic reduction its costs have boosted new unpredictable developments in field plant pathology. cost whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a branch, phylogenomics, being developed. Fungal taxonomy deeply influenced by genome comparison, too. It easier to discover genes as potential targets an accurate diagnosis or emerging pathogens, notably those quarantine concern. Similarly, with development metabarcoding metagenomics techniques, it possible unravel complex diseases answer crucial questions, such “What’s my soil?”, good approximation, including fungi, bacteria, nematodes, etc. technologies allow redraw approach disease control strategies considering pathogens within their environment deciphering interactions between microorganisms cultivated crops. This kind analysis usually generates big data need sophisticated bioinformatic tools (machine learning, artificial intelligence) management. Herein, examples use research fungal diversity some are reported.

Language: Английский

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