Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

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

Published: Feb. 19, 2021

The number of microbiome-related studies has notably increased the availability data on human microbiome composition and function. These provide essential material to deeply explore host-microbiome associations their relation development progression various complex diseases. Improved data-analytical tools are needed exploit all information from these biological datasets, taking into account peculiarities data, i.e., compositional, heterogeneous sparse nature datasets. possibility predicting host-phenotypes based taxonomy-informed feature selection establish an association between predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights models that can be used outputs, such as classification prediction in microbiology, infer host phenotypes diseases use microbial communities stratify patients by characterization state-specific signatures. Here we review state-of-the-art ML methods respective software applied studies, performed part COST Action ML4Microbiome activities. This scoping focuses application related clinical diagnostics, prognostics, therapeutics. Although presented here more bacterial community, many algorithms could general, regardless type. literature covering broad topic aligned with methodology. manual identification sources been complemented with: (1) automated publication search through digital libraries three major publishers using natural language processing (NLP) Toolkit, (2) relevant repositories GitHub ranking research papers relying rank approach.

Language: Английский

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AlphaFold, Artificial Intelligence (AI), and Allostery

The Journal of Physical Chemistry B, Journal Year: 2022, Volume and Issue: 126(34), P. 6372 - 6383

Published: Aug. 17, 2022

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). appended projects research directions. The database it been creating promises an untold number applications with vast potential impacts are still difficult to surmise. AI approaches can revolutionize personalized treatments usher in better-informed clinical trials. They promise make giant leaps toward reshaping revamping drug discovery strategies, selecting prioritizing combinations targets. Here, we briefly overview structural biology, including molecular dynamics simulations prediction microbiota-human protein-protein interactions. We highlight advancements accomplished by deep-learning-powered protein structure their impact on life sciences. At same time, does not resolve decades-long folding challenge, nor identify pathways. models provides do capture conformational mechanisms like frustration allostery, which rooted ensembles, controlled dynamic distributions. Allostery signaling properties populations. also generate ensembles intrinsically disordered proteins regions, instead describing them low probabilities. Since generates single ranked structures, rather than cannot elucidate allosteric activating driver hotspot mutations resistance. However, capturing key features, deep learning techniques use predicted conformation as basis for generating a diverse ensemble.

Language: Английский

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Black soldier fly larvae for organic manure recycling and its potential for a circular bioeconomy: A review

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 833, P. 155122 - 155122

Published: April 9, 2022

Language: Английский

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Machine learning for microbiologists

Nature Reviews Microbiology, Journal Year: 2023, Volume and Issue: 22(4), P. 191 - 205

Published: Nov. 15, 2023

Language: Английский

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Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome

World Journal of Gastroenterology, Journal Year: 2022, Volume and Issue: 28(4), P. 412 - 431

Published: Jan. 19, 2022

Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained gastrointestinal symptoms, recently described as disturbance of the microbiota-gut-brain axis. Despite decades research, pathophysiology this highly heterogeneous disorder remains elusive. However, dramatic change in understanding underlying pathophysiological mechanisms surfaced when importance gut microbiota protruded scientific picture. Are we getting any closer to IBS' etiology, or are drowning unspecific, conflicting data because possess limited tools unravel cluster secrets our concealing? In comprehensive review discussing some major important features IBS and their interaction with microbiota, microbiota-altering treatment such low FODMAP diet fecal transplantation, neuroimaging methods analyses, current future challenges big analysis IBS.

Language: Английский

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Dealing with dimensionality: the application of machine learning to multi-omics data

Bioinformatics, Journal Year: 2023, Volume and Issue: 39(2)

Published: Jan. 11, 2023

Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order support human users data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts omics data produced next generation sequencing and other -omics assays. A systematic assessment current literature can help identify key trends potential gaps methodology applications. We surveyed on ML multi-omic integration quantitatively explored goals, techniques involved this field. were particularly interested examining how researchers use deal with volume complexity these datasets.Our main finding that used those address challenges few samples many features. Dimensionality reduction reduce feature count alongside models also appropriately handle relatively samples. Popular include autoencoders, random forests vector machines. found field heavily influenced The Cancer Genome Atlas dataset, which accessible contains diverse experiments.All processing scripts available at GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or Zenodo: https://doi.org/10.5281/zenodo.7361807.Supplementary Bioinformatics online.

Language: Английский

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Machine learning approaches in microbiome research: challenges and best practices

Frontiers in Microbiology, Journal Year: 2023, Volume and Issue: 14

Published: Sept. 22, 2023

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, modeling, performance estimation, model interpretation, and the extraction of biological information from results. To assist decision-making, we offer set recommendations on algorithm pipeline creation evaluation, stemming COST Action ML4Microbiome. We compared suggested approaches multi-cohort shotgun metagenomics dataset colorectal cancer patients, focusing their in disease diagnosis biomarker discovery. It is demonstrated that use compositional transformations filtering methods as part preprocessing does not always improve model. In contrast, multivariate such Statistically Equivalent Signatures algorithm, was effective reducing classification error. When validated separate test dataset, this combination with random forest provided most accurate estimates. Lastly, showed how linear modeling by logistic regression coupled visualization techniques Individual Conditional Expectation (ICE) plots can yield interpretable results insights. These findings are significant for clinicians non-experts alike translational applications.

Language: Английский

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Pathways to engineering the phyllosphere microbiome for sustainable crop production

Nature Food, Journal Year: 2022, Volume and Issue: 3(12), P. 997 - 1004

Published: Dec. 5, 2022

Language: Английский

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Fast detection of bacterial gut pathogens on miniaturized devices: an overview

Expert Review of Molecular Diagnostics, Journal Year: 2024, Volume and Issue: 24(3), P. 201 - 218

Published: Feb. 13, 2024

Introduction Gut microbes pose challenges like colon inflammation, deadly diarrhea, antimicrobial resistance dissemination, and chronic disease onset. Development of early, rapid specific diagnosis tools is essential for improving infection control. Point-of-care testing (POCT) systems offer rapid, sensitive, low-cost sample-to-answer methods microbe detection from various clinical environmental samples, bringing the advantages portability, automation, simple operation.

Language: Английский

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Establishment of a consensus protocol to explore the brain pathobiome in patients with mild cognitive impairment and Alzheimer's disease

Alzheimer s & Dementia, Journal Year: 2023, Volume and Issue: 19(11), P. 5209 - 5231

Published: June 7, 2023

Abstract Microbial infections of the brain can lead to dementia, and for many decades microbial have been implicated in Alzheimer's disease (AD) pathology. However, a causal role infection AD remains contentious, lack standardized detection methodologies has led inconsistent detection/identification microbes brains. There is need consensus methodology; Pathobiome Initiative aims perform comparative molecular analyses post mortem brains versus cerebrospinal fluid, blood, olfactory neuroepithelium, oral/nasopharyngeal tissue, bronchoalveolar, urinary, gut/stool samples. Diverse extraction methodologies, polymerase chain reaction sequencing techniques, bioinformatic tools will be evaluated, addition direct culture metabolomic techniques. The goal provide roadmap detecting infectious agents patients with mild cognitive impairment or AD. Positive findings would then prompt tailoring antimicrobial treatments that might attenuate remit mounting clinical deficits subset patients.

Language: Английский

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