AIMC Topic: SARS-CoV-2

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An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BMC biology
BACKGROUND: Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs...

Evaluating community resilience through social media during China's first post-COVID-19 reopening: insights from machine learning.

Journal of global health
BACKGROUND: In the face of pandemics from infectious diseases, enhancing community resilience is increasingly important. It is, therefore, essential to evaluate community resilience and identify factors that can strengthen it. This study aimed to eva...

Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.

PloS one
Respiratory sound analysis has emerged as a promising approach for detecting and diagnosing respiratory diseases, including COVID-19. This study investigates using OpenSMILE features for COVID-19 detection using vowel speech sounds /a/, /e/, and /o/ ...

A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.

Scientific reports
Due to the widespread COVID-19 vaccinations, we are focusing more on side effects to immunizations that might affect people's perceptions, and ultimately vaccine hesitancy. Machine learning (ML)-based predictive models using individual-level data ser...

Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.

Antimicrobial resistance and infection control
BACKGROUND: The transmission of antibiotic-resistant bacteria in intensive care units (ICUs) poses a significant challenge to infection control and patient safety. While direct patient-to-patient transmission is well documented, the relative contribu...

scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification.

BMC bioinformatics
Accurate cell type classification is critical for downstream analysis in single-cell RNA sequencing (scRNA-seq). Most existing methods rely on a single type of feature representation-such as statistical, information theory, matrix factorization, or d...

A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic.

Scientific reports
Mental health disorders, such as depression and anxiety, are increasing, and thus, there is a necessity for accurate and effective detection. K-Nearest Neighbors (KNN) and extensions have been extensively used in disease detection. In this work, Adap...

Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.

JMIR nursing
BACKGROUND: The COVID-19 pandemic exposed systemic vulnerabilities in public health infrastructure, underscoring the urgency for innovation in disease surveillance and emergency response. Artificial intelligence (AI) has emerged as a promising tool t...

AI-designed PNA-peptide chimera overcomes suboptimal binding for dual inhibition of viral RdRp.

European journal of medicinal chemistry
The chimera combining the peptide nucleic acids (PNAs) and peptides represent a promising bifunctional strategy by concurrently binding with protein catalytic pocket and its associated RNA template, effectively disrupting protein's function. Conventi...

Predictive surveillance and diagnosis of COVID-19: An integrative machine learning and wastewater multi-omics approach.

Water research
COVID-19 has had major global impacts, highlighting the importance of robust predictive surveillance and diagnostic systems to ensure effective public health responses. Traditional surveillance methods based on passive case counting and diagnostic te...