Machine learning and deep learning applications in microbiome research

ISME Communications, Journal Year: 2022, Volume and Issue: 2(1)

Published: Oct. 6, 2022

Abstract The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern influence macroscopic systems including human health, plant resilience, biogeochemical cycling. Such feats have attracted interest scientific community, which has recently turned to machine learning deep methods interrogate microbiome elucidate relationships between its composition function. Here, we provide an overview of how latest studies harness inductive prowess artificial intelligence methods. We start by highlighting that data – being compositional, sparse, high-dimensional necessitates special treatment. then introduce traditional novel discuss their strengths applications. Finally, outlook pipelines, focusing on bottlenecks considerations address them.

Language: Английский

---

Machine learning for data integration in human gut microbiome

Microbial Cell Factories, Journal Year: 2022, Volume and Issue: 21(1)

Published: Nov. 23, 2022

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led an explosion of different types molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis machine learning algorithms shown be useful for identifying key signatures, discovering potential patient stratifications, particularly generating models can accurately predict phenotypes. In this review, we first discuss how dysbiosis intestinal is linked disease development modulation strategies ecosystem used treatment. addition, introduce categories workflows approaches, they perform integrative multi-omics data. Finally, review advances microbiome applications related challenges. Based on conclude very well suited these approaches microbe-targeted therapies, ultimately help achieving personalized precision medicine.

Language: Английский

---

A Mini Review of Node Centrality Metrics in Biological Networks

International Journal of Network Dynamics and Intelligence, Journal Year: 2022, Volume and Issue: unknown, P. 99 - 110

Published: Dec. 22, 2022

Survey/review study A Mini Review of Node Centrality Metrics in Biological Networks Mengyuan Wang 1,2, Haiying 1, and Huiru Zheng 1,* 1 School Computing, Ulster University, Belfast, BT15 1ED, United Kingdom 2 Scotland’s Rural College, Edinburgh, EH25 9RG, * Correspondence: [email protected] Received: 31 October 2022 Accepted: 21 November Published: 22 December Abstract: The diversity nodes a complex network causes each node to have varying significance, the important often significant impact on structure function network. Although interpretation results biological networks must always depend topological nodes, there is presently no consensus how use these metrics, most analyses result basic limited number metrics. To thoroughly comprehend networks, it necessary consistently understand notion centrality. Therefore, for 10 typical nodal metrics first assesses their current applications, advantages, disadvantages as well potential applications. Then, review previous studies provided, suggestions are made correspondingly purpose improving topology algorithms. Finally, following recommendations this study: (1) comprehensive accurate assessment centrality necessitates multiple including both target its surroundings, density maximum neighbourhood component(DMNC) can be used complement other metrics; (2) different applied identify with functions, which mapped modular bridging roles, susceptibility; (3) groups verified against other, degree component (MNC), eccentricity, closeness radiality; stress betweenness.

Language: Английский

---

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 14804 - 14831

Published: Jan. 1, 2023

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and (ML) are combined to solve critical problems. Usually, works use single modality, such as images, audio, text, signals. However, real-world issues have become now, handling them using of instead modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements the challenges MML, namely: representation, translation, alignment, fusion co-learning, presents gaps challenges. A systematic literature review (SLR) applied define progress trends on those domain. In total, 1032 articles were examined this extract features like source, domain, application, etc. article will help researchers understand constant state navigate selection future directions.

Language: Английский

---

Deciphering the gut microbiome: The revolution of artificial intelligence in microbiota analysis and intervention

Current Research in Biotechnology, Journal Year: 2024, Volume and Issue: 7, P. 100211 - 100211

Published: Jan. 1, 2024

The human gut microbiome is an intricate ecosystem with profound implications for host metabolism, immune function, and neuroendocrine activity. Over the years, studies have strived to decode this microbial universe, especially its interactions health underlying metabolic processes. Traditional analyses often struggle complex interplay within due presumptions of independence. In response, machine learning (ML) deep (DL) provide advanced multivariate non-linear analytical tools that adeptly capture microbiota. With influx data from metagenomic next-generation sequencing (mNGS), there's increasing reliance on these artificial intelligence (AI) subsets derive actionable insights. This review delves into cutting-edge ML techniques tailored microbiota research. It further underscores potential in shaping clinical diagnostics, prognosis, intervention strategies, pointing a future where computational methods bridge gap between knowledge targeted interventions.

Language: Английский

---

Understanding gut microbiome‐based machine learning platforms: A review on therapeutic approaches using deep learning

Chemical Biology & Drug Design, Journal Year: 2024, Volume and Issue: 103(3)

Published: March 1, 2024

Abstract Human beings possess trillions of microbial cells in a symbiotic relationship. This relationship benefits both partners for long time. The gut microbiota helps many bodily functions from harvesting energy digested food to strengthening biochemical barriers the and intestine. But changes composition bacteria that can enter gastrointestinal tract cause infection. Several approaches like culture‐independent techniques such as high‐throughput meta‐omics projects targeting 16S ribosomal RNA (rRNA) sequencing are popular methods investigate human taxonomically characterizing communities. conformation diversity should be provided by whole‐genome shotgun metagenomic site‐specific community DNA associating genome mapping, gene inventory, metabolic remodelling reformation, ease functional study microbiota. Preliminary examination therapeutic potency dysbiosis‐associated diseases permits investigation pharmacokinetic‐pharmacodynamic communities escalation treatment dosage plan. Gut microbiome is an integration metagenomics which has influenced field last two decades. And incorporation artificial intelligence deep learning through “omics‐based” microfluidic evaluation enhanced capability identification thousands microbes.

Language: Английский

---

A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance

Diabetes & Metabolic Syndrome Clinical Research & Reviews, Journal Year: 2024, Volume and Issue: 18(4), P. 103000 - 103000

Published: April 1, 2024

Language: Английский

---

Microbiota during pregnancy and early life: role in maternal−neonatal outcomes based on human evidence

Gut Microbes, Journal Year: 2024, Volume and Issue: 16(1)

Published: Aug. 19, 2024

Here, we explored the vast potential of microbiome-based interventions in preventing and managing non-communicable diseases including obesity, diabetes, allergies, celiac disease, inflammatory bowel diseases, malnutrition, cardiovascular across different life stages. We discuss intricate relationship between microbiome emphasizing on "window opportunity" for microbe–host interactions during first years after birth. Specific biotics also live biotherapeutics fecal microbiota transplantation emerge as pivotal tools precision medicine, acknowledging "one size doesn't' fit all" aspect. Challenges implementation underscore need advanced technologies, scientific transparency, public engagement. Future perspectives advocate understanding maternal−neonatal microbiome, exploring maternal exposome delving into human milk's role establishment restoration infant its influence over health disease. An integrated approach, employing multi-omics accounting inter-individual variance composition function appears central to unleash full early-life revolutionizing healthcare.

Language: Английский

---

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction

Genome Medicine, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 31, 2023

Abstract Background Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied phenotype prediction at different scales, but due to black-box nature of learning, integrating modalities interpreting biological challenging. Additionally, partial availability presents a challenge developing predictive models. Method To address challenges, we developed DeepGAMI, an interpretable neural network model improve genotype–phenotype from data. DeepGAMI leverages functional genomic information, such as eQTLs gene regulation, guide connections. it includes auxiliary layer cross-modal imputation allowing latent features missing thus predicting phenotypes single modality. Finally, uses integrated gradient prioritize various phenotypes. Results We several datasets including genotype bulk cell-type expression diseases, electrophysiology mouse neuronal cells. Using cross-validation independent validation, outperformed existing classifying types, clinical even using (e.g., AUC score 0.79 Schizophrenia 0.73 cognitive impairment Alzheimer’s disease). Conclusion demonstrated that improves prioritizes phenotypic networks multiple complex brains diseases. Also, prioritized disease-associated variants, genes, regulatory linked providing novel insights into interpretation mechanisms. is open-source available general use.

Language: Английский

---

Deep learning in microbiome analysis: a comprehensive review of neural network models

Frontiers in Microbiology, Journal Year: 2025, Volume and Issue: 15

Published: Jan. 22, 2025

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to integration deep learning (DL) methods. These computational techniques have become essential for addressing inherent complexity and high-dimensionality microbiome data, which consist different types omics datasets. Deep algorithms shown remarkable capabilities pattern recognition, feature extraction, predictive modeling, enabling researchers uncover hidden relationships within ecosystems. By automating detection functional genes, interactions, host-microbiome dynamics, DL methods offer unprecedented precision understanding composition its impact on health, disease, environment. However, despite their potential, approaches face challenges research. Additionally, biological variability datasets requires tailored ensure robust generalizable outcomes. As research continues generate vast complex datasets, these will be crucial advancing microbiological insights translating them into practical applications with DL. This review provides an overview models discussing strengths, uses, implications future studies. We examine how are being applied solve key problems highlight potential pathways overcome current limitations, emphasizing transformative could field moving forward.

Language: Английский

---