Vaccines

Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya

Background: Limited data utilization in low-resource settings poses a barrier to the vaccine delivery ecosystem, undermining efforts to achieve equitable immunization coverage. In nomadic populations, individuals face an increased risk of missing crucial vaccination doses as children. One such population is the Maasai in Narok County, Kenya, where the absence of high-volume, quality data hampers a...

Geometry Reinforced Efficient Attention Tuning Equipped with Normals for Robust Stereo Matching

Despite remarkable advances in image-driven stereo matching over the past decade, Synthetic-to-Realistic Zero-Shot (Syn-to-Real) generalization remains an open challenge. This suboptimal generalization performance mainly stems from cross-domain shifts and ill-posed ambiguities inherent in image textures, particularly in occluded, textureless, repetitive, and non-Lambertian (specular/transparent) r...

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer

Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is curren...

Fine scale structural information substantially improves multivariate regression model for mRNA in-vial degradation prediction

The success of COVID-19 mRNA vaccines has made the in-solution stability optimization of mRNAs a key objective. However, we still lack a complete unde...

Predicting COVID-19 incidence from seroprevalence and population-based cohort data using interpretable machine learning with differential privacy analysis

During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although ...

Predicting Infant Nonattendance at the Next Recommended Well-Child Visit: Model Development and Validation

BackgroundWell-child visits (WCVs) are essential for preventive care, yet missed appointments often lead to delayed interventions. We developed and va...

Designing mRNA coding sequence via multimodal reverse translation language modeling with Pro2RNA

mRNA coding sequence design is a critical component in the development of mRNA vaccines, nucleic acid therapeutics, and heterologous gene expression s...

RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization

Messenger RNA (mRNA) vaccines offer promising therapeutics for combating various diseases, yet their inherent chemical instability hampers their long-...

Machine Learning Enabled Smartphone CRISPR-Cas12a Lateral Flow Platform for Sensitive Detection of Circulating HPV DNA

Persistent infection with high-risk human papillomavirus (HPV) is the primary cause of cervical cancer and other HPV-related malignancies. Effective s...

A Universal, AI-based Design Framework for Efficient Manufacturing of mRNA Therapeutics

The growth of mRNA therapeutics is limited by bespoke manufacturing processes. To overcome this barrier to access and innovation, we introduce an AI-d...

Predictors of COVID-19 hospital outcomes: a machine learning analysis of the National COVID Cohort Collaborative

Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet hete...

Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology

Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whol...

Leveraging large language models to address common vaccination myths and misconceptions

Large language models (LLMs) are increasingly used by the public to seek health information, yet their reliability in addressing common vaccine myths ...

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vacc...

MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning

Recent progress in the reasoning capabilities of multimodal large language models (MLLMs) has empowered them to address more complex tasks such as sci...

Biomedical Large Language Models and Prompt Engineering for Causality Assessment of Individual Case Safety Reports in Pharmacovigilance

Background: Biomedical Large Language Models (LLMs) combined with prompt engineering offer domain-specific reasoning, yet their application to individ...

Automated Model Discovery Based on COVID-19 Epidemiologic Data

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative appr...

Genomic Evolution of SARS-CoV-2 Delta Variants Pre- and Post-Omicron Emergence using Alignment-free Machine Learning models

The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its enhanced transmissibility and vaccine-escape mutatio...

Mechanistic machine learning enables interpretable and generalizable prediction of prime editing outcomes

Although prime editing (PE) can effect virtually any specified local change to genomic DNA in living systems, its efficient application currently requ...

Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection

Recent advances in diffusion models have enabled powerful image editing capabilities guided by natural language prompts, unlocking new creative possib...