Public Health

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models.

Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelli...

Assessment and classification of COVID-19 DNA sequence using pairwise features concatenation from multi-transformer and deep features with machine learning models.

The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such a major viral outbreak demands early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. The emerging global infectious COVID-19 disease by novel Sev...

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance ...

Respiratory Syncytial Virus Vaccine Design Using Structure-Based Machine-Learning Models.

When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection...

Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.

The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate ...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination o...

The Surgical Clerkship in the COVID Era: A Natural Language Processing and Thematic Analysis.

INTRODUCTION: Responses to COVID-19 within medical education prompted significant changes to the surgical clerkship. We analyzed the changes in medica...

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various ot...

Predicting malaria outbreak in The Gambia using machine learning techniques.

Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic g...

A Retrospective Analysis of Indoor CO Measurements Obtained with a Mobile Robot during the COVID-19 Pandemic.

This work presents a retrospective analysis of indoor CO measurements obtained with a mobile robot in an educational building after the COVID-19 lockd...

Artificial intelligence for dysplasia detection during surveillance colonoscopy in patients with ulcerative colitis: A cross-sectional, non-inferiority, diagnostic test comparison study.

BACKGROUND AND STUDY AIM: High-definition virtual chromoendoscopy, along with targeted biopsies, is recommended for dysplasia surveillance in ulcerati...

Predicting the age of field mosquitoes using mass spectrometry and deep learning.

Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector surveillance is needed. Survival of mosquitoes is key for epid...

Machine learning based DNA melt curve profiling enables automated novel genotype detection.

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, p...

Predicting drug-Protein interaction with deep learning framework for molecular graphs and sequences: Potential candidates against SAR-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 disease, which represents a new life-threatening disaster. Regard...

Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches.

To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing the microfluidic conditions and lipid mix ratios of messenger RNA...

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays ...

Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach.

BACKGROUND: SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, wheth...

Optical biosensing of monkeypox virus using novel recombinant silica-binding proteins for site-directed antibody immobilization.

The efficient immobilization of capture antibodies is crucial for timely pathogen detection during global pandemic outbreaks. Therefore, we proposed a...

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to e...

Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis.

BACKGROUND: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given t...