Attention-Based Hybrid Deep Learning Models for Classifying COVID-19 Genome Sequences

AI, Journal Year: 2025, Volume and Issue: 6(1), P. 4 - 4

Published: Jan. 2, 2025

Background: COVID-19 genetic sequence research is crucial despite immunizations and pandemic control. COVID-19-causing SARS-CoV-2 must be understood genomically for several reasons. New viral strains may resist vaccines. Categorizing sequences helps researchers track changes assess immunization efficacy. Classifying genome with other viruses to understand its evolution interactions illnesses. Methods: The proposed study introduces a deep learning-based genomic categorization approach. Attention-based hybrid learning (DL) models categorize 1423 11,388 sequences. An unknown dataset also used the models. five models’ accuracy, f1-score, area under curve (AUC), precision, Matthews correlation coefficient (MCC), recall are evaluated. Results: results indicate that Convolutional neural network (CNN) Bidirectional long short-term memory (BLSTM) attention layer (CNN-BLSTM-Att) achieved an accuracy of 99.99%, which outperformed For external validation, model shows 99.88%. It reveals DL-based approaches can accurately classify high degree accuracy. This method might assist in identifying classifying virus clinical situations. Immunizations have lowered danger, but categorizing global health activities plan recurrence or future threats.

Language: Английский

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The Impact of Microbiota on Neurological Disorders: Mechanisms and Therapeutic Implications

OBM Neurobiology, Journal Year: 2025, Volume and Issue: 09(01), P. 1 - 12

Published: Feb. 28, 2025

Interactions in the gut-brain crosstalk have led to development of an entirely new concept: "microbiota-gut-brain axis". Microbiota has gained considerable attention relation disorders a more neurological nature, such as neurodevelopmental and neuropsychiatric illnesses like autism spectrum disorder, anxiety, mood disorders. This review aims summarize recent trends insights into role consequences gut microbiota brain health pediatric Dysbiosis may be associated with increased risk diseases that lead different disruptions conditions, including mental issues. During dysbiosis, neuropsychological stress hormones usually affect oxytocin GABA neurons are significantly reduced. Current studies report major depression, cognitive dysfunction closely dysbiosis. In last few years, handful clinical emerged, illustrating potential for bidirectional relationship interactions humans. Perhaps some most crucial investigations demonstrating overlapping relationships human axis come from trials focusing on modulating noting significant correlates. A field is emerging gene-editing technology could represent tool improve microbial characteristics. approach particularly relevant neurodegenerative brain-gut linked loss species and/or high pathobiont load.

Language: Английский

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Understanding dysbiosis and resilience in the human gut microbiome: biomarkers, interventions, and challenges

Frontiers in Microbiology, Journal Year: 2025, Volume and Issue: 16

Published: March 4, 2025

The healthy gut microbiome is important in maintaining health and preventing various chronic metabolic diseases through interactions with the host via different gut–organ axes, such as gut-brain, gut-liver, gut-immune, gut-lung axes. human relatively stable, yet can be influenced by numerous factors, diet, infections, diseases, medications which may disrupt its composition function. Therefore, microbial resilience suggested one of key characteristics a humans. However, our understanding definition indicators remains unclear due to insufficient experimental data. Here, we review impact drivers including intrinsic extrinsic factors diet antibiotics on microbiome. Additionally, discuss concept resilient highlight potential biomarkers diversity indices some bacterial taxa recovery-associated bacteria, resistance genes, antimicrobial peptides, functional flexibility. These facilitate identification prediction microbiomes, particularly precision medicine, diagnostic tools or machine learning approaches especially after that cause stable dysbiosis. Furthermore, current nutrition intervention strategies maximize resilience, challenges investigating future directions this field research.

Language: Английский

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Progressing microbial genomics: Artificial intelligence and deep learning driven advances in genome analysis and therapeutics

Intelligence-Based Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100251 - 100251

Published: April 1, 2025

Language: Английский

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AI-Powered Clinical Trial Design With Translational Bioinformatics

Advances in medical diagnosis, treatment, and care (AMDTC) book series, Journal Year: 2024, Volume and Issue: unknown, P. 57 - 72

Published: June 30, 2024

Clinical trial design is undergoing a revolution fueled by artificial intelligence (AI) and translational bioinformatics. This chapter explores how AI techniques like machine learning deep are being harnessed to analyze vast datasets of biological clinical information. By integrating these insights with bioinformatics, researchers can identify promising drug candidates, select patients most likely benefit from treatment, more efficient targeted trials. Real-world examples showcase the application in immuno-oncology patient selection, discovery for rare diseases, predicting Alzheimer's outcomes, virtual recruitment cardiovascular studies. While challenges data quality ethical considerations exist, bioinformatics hold immense promise accelerating development, bringing life-saving therapies faster.

Language: Английский

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Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Nov. 15, 2024

Introduction The integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution trends AI applications this domain, providing insights into how is transforming practice microbiology. Methods We employed bibliometric analysis topic modeling examine 27,420 publications from Web Science Core Collection, covering period 2010 2024. These methods enabled us identify key trends, areas, geographical distribution efforts. Results Since 2016, there been an exponential increase AI-related publications, with significant contributions China USA. Our identified eight major application areas: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, data management systems. Notably, we found lexical overlaps between these especially drug suggesting interconnected landscape. Discussion increasingly moving laboratory clinical applications, enhancing hospital operations public health strategies. It plays a vital role optimizing improving diagnostic speed, treatment efficacy, disease control, particularly through advancements rapid susceptibility testing COVID-19 development. highlights current status, progress, challenges microbiology, guiding future directions, resource allocation, policy-making.

Language: Английский

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