COVID-19

Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets

Recent work on computer vision and image processing has relied substantially on open datasets, which allow for an objective comparison of techniques and methodologies. In the area of computational pathology and, more specifically, on colorectal cancer, the dataset NCT-CRC-HE-100K, which consists of 100,000 patches of human tissue stained with Haematoxylin and Eosin has been widely used as a traini...

DoBSeqWF: A framework for sensitive detection of individual genetic variation in pooled sequencing data

Population screening for rare genetic diseases is limited by the high cost of next- generation sequencing. Double-batched sequencing (DoBSeq) is a cost-effective method for assigning rare variants to individuals using two-dimensional unique double- pooled sequencing. However, this method produces complex, high-depth sequencing data that requires a specialized workflow for efficient and reproducibl...

Silencer variants are key drivers of gene upregulation in Alzheimer’s disease

Alzheimer’s disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial herit...

Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases in UK Biobank

Cardiometabolic diseases are multifactorial disorders influenced by numerous genetic variants and their complex interactions. Although recent studies ...

Creation of an Open-Access Lung Ultrasound Image Database For Deep Learning and Neural Network Applications

Lung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray a...

VADEr: Vision Transformer-Inspired Framework for Polygenic Risk Reveals Underlying Genetic Heterogeneity in Prostate Cancer

Polygenic risk scores (PRSs) serve as quantitative metrics of genetic liability for various conditions. Traditionally calculated as an effect size wei...

Using Artificial Intelligence (AI) to Model Clinical Variant Reporting for Next Generation Sequencing (NGS) Oncology Assays

Targeted next generation sequencing (NGS) of somatic DNA is now routinely used for diagnostic and predictive reporting in the oncology clinic. The exp...

Predicting Bacterial Vaginosis Development using Artificial Neural Networks

Bacterial vaginosis (BV) is a dysbiosis of the vaginal microbiome, characterized by the depletion of protective Lactobacillus spp. and overgrowth of a...

DISCO: A Delta-NIHSS-Based Machine Learning Model for Predicting Recurrence, Disability, and Mortality Following Acute Ischemic Stroke

An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for ...

Reinforcement learning-based control of epidemics on networks of communities and correctional facilities

Correctional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can ...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). We conducted a re...

A Methodology Framework for Analyzing Health Misinformation to Develop Inoculation Intervention Using Large Language Models: A case study on covid-19

The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impac...

Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, includin...

A comparative analysis of dengue, chikungunya, and Zika in a pediatric cohort over 18 years

Dengue, chikungunya, and Zika are diseases of major human concern. Differential diagnosis is complicated in children and adolescents by their overlapp...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025)...

Clinical Evaluation of an AI System for Streamlined Variant Interpretation in Genetic Testing

The growing use of exome/genome sequencing to diagnose hereditary diseases has increased the interpretive workload for clinical laboratories. Efficien...

Identification of (ultra-)rare functional promoter mutations in cancer using sequence-based deep learning models

The identification of non-coding somatic cancer-driver mutations remains challenging due to difficulties in interpreting rare and ultra-rare variants....

AI-Powered Social Robots for Addressing Loneliness: A Systematic Review Protocol

Loneliness is a significant public health concern that affects millions of people worldwide, particularly older adults. With advancements in artificia...

Precision Grounding: Augmenting Large Language Models with Evidence-Based Databases for Trustworthy Genetic Variant Summarization

Accurate interpretation of genetic variants is critical for precision medicine. While large language models (LLMs) show promise for summarization, the...

Multiple instance fine-mapping: predicting causal regulatory variants with a deep sequence model

Identifying causal genetic variants in a computational manner remains an open problem. Training end-to-end prediction models is not possible without l...