Vaccines

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health.

COVID-19 vaccinations and their side effects: a scoping systematic review.

The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have begun to prepare a vaccine that can eliminate the virus and restore balance to life in the entire world. Over the past two years, countries and specialized companies have competed to obtain a license from the World Health Organization for the vaccin...

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the...

Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning.

Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA de...

A Preliminary Study to Evaluate the Immune Response to a Booster Dose of the Adult Tetanus-Diphtheria Vaccine (Td) Available in Iran.

BACKGROUND: Despite the availability of tetanus-diphtheria (TD) vaccines in Iran, the seroconversion rate of these products as a booster dose is unkno...

Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.

PURPOSE: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening ...

A Physics-Informed Neural Network approach for compartmental epidemiological models.

Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forec...

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory sy...

COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Algorithms.

BACKGROUND: There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work fo...

Current limitations in predicting mRNA translation with deep learning models.

BACKGROUND: The design of nucleotide sequences with defined properties is a long-standing problem in bioengineering. An important application is prote...

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of myc...

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines.

With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines ...

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.

BACKGROUND: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and gener...

VacSol-ML(ESKAPE) Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and...

Cooperating Graph Neural Networks With Deep Reinforcement Learning for Vaccine Prioritization.

This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing vaccine dist...

Identifying psychological predictors of SARS-CoV-2 vaccination: A machine learning study.

BACKGROUND: Major barriers to addressing SARS-CoV-2 vaccine hesitancy include limited knowledge of what causes delay/refusal of SARS-CoV-2 vaccination...

An architecture for COVID-19 analysis and detection using big data, AI, and data architectures.

The COVID-19 epidemic is affecting individuals in many ways and continues to spread all over the world. Vaccines and traditional medical techniques ar...

The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.

HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are available, they do not encompass all subtypes. Meanwhile,...

AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.

Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA t...

COVID-19 prevention and control effect of non-pharmaceutical interventions-fuzzy-sets qualitative comparative analysis based on 69 countries in the world.

INTRODUCTION: Coronavirus disease 2019 occurred unexpectedly in late December 2019, it was difficult to immediately develop an effective vaccine or pr...