COVID-19

Serum metabolic signatures are associated with anti-drug antibody development in rheumatoid arthritis patients treated with adalimumab

Development of anti-drug antibodies (ADAs) is a barrier to long-term efficacy of biologic therapies in rheumatoid arthritis (RA), but no biomarkers exist to predict ADA formation. This study explored the potential of serum metabolomics to predict development of ADAs to adalimumab in patients with RA. Serum from patients with RA (n=47), treatment naïve for tumour necrosis factor-alpha inhibitor the...

Evaluation of Gender Bias in the Evaluation of Synthetic Cardiovascular Disease Cases with Open Source LLMs

To systematically evaluate gender bias in open-source large language models (LLMs) for cardiovascular diagnostic decision-making using controlled synthetic case vignettes. We generated 500 synthetic cardiovascular cases with randomly assigned gender (male/female, equal distribution) and age (45-80 years), keeping all other clinical variables identical. Two structured prompts simulated sequential c...

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to ...

Radiologist-AI Collaboration for Ischemia Diagnosis in Small Bowel Obstruction: Multicentric Development and External Validation of a Multimodal Deep Learning Model

To develop and externally validate a multimodal AI model for detecting ischaemia complicating small-bowel obstruction (SBO). We combined 3D CT data wi...

Temperature dominates dengue transmission in Thailand: Machine learning reveals critical thresholds and COVID-19 disruption

Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental a...

Refining the genetic landscape of anophthalmia and microphthalmia: a comprehensive framework with deep learning and updated gene panels

Anophthalmia and microphthalmia (A/M) are rare congenital eye disorders with a low molecular diagnosis rate, which limits clinical management and gene...

Leveraging Open-Source Large Language Models to Identify Undiagnosed Patients with Rare Genetic Aortopathies

Rare genetic aortopathies are frequently undiagnosed due to phenotypic heterogeneity, and delayed diagnosis can lead to fatal cardiac outcomes. While ...

Advancing Human Population Genomics with DNA Foundation Models

DNA foundation models offer a new approach to interpret genetic variation, but their potential in population-scale genomics remains untapped. We intro...

VarDrug: A Machine Learning Approach for Variant-Drug Interaction, Application to Drugs for Psychiatric Disorders

Predicting variant-drug interactions is essential for advancing precision medicine across therapeutic areas. The Pharmacogenomics Knowledge Base (Phar...

Epistatic contributions to human traits via transcription factor mechanisms

Epistasis causes an individual’s genetic background to modulate a DNA variant’s effect on trait [1–6]. Epistatic interactions among different loci in ...

aiDIVA – Diagnostics of Rare Genetic Diseases Using Large Language Models

Genome sequencing (GS) enables the accurate identification of genetic variants in most genomic regions and is rapidly transforming routine diagnostics...

Candidate Correlates of Protection in the HVTN505 HIV-1 Vaccine Efficacy Trial Identified by Positive-Unlabeled Learning

With a goal of unveiling mechanisms by which vaccines can provide protection against HIV-1 acquisition, several studies have explored correlates of ri...

Statistical, Multi-scale and Attention-based Layer Pooling of Wav2Vec-2 Speech Embeddings for Parkinson’s Disease Detection

Self-supervised pre-trained speech models such as wav2vec 2.0 provide rich frame-level embeddings that are increasingly used for clinical voice screen...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can su...

ViraLite: An Ultracompact HIV Viral Load Self-Testing System with Internal Quality Control

The availability of effective antiretroviral therapy has made HIV manageable, provided patients have consistent access to routine viral load (VL) test...

Machine Learning Prediction of Pharmacogenetic Test Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: A Retrospective Cohort Study

Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due ...

SAHDAI-XAI Subarachnoid Hemorrhage Detection Artificial Intelligence- eXplainable AI: Testing explainability in SAH Imaging Data and AI Modeling

Subarachnoid hemorrhage (SAH) is a life-threatening and crucial neurological emergency. SAHDAI-XAI (Subarachnoid Hemorrhage Detection Artificial Intel...

A machine-learning framework to characterize functional disease architectures and prioritize disease variants

Modeling disease effect sizes from genome-wide association studies (GWAS) is critical for both advancing our understanding of the functional architect...

Identification and validation of tolerogenic dendritic cells-related biomarkers in diabetic retinopathy

Diabetic retinopathy (DR) is a primary microvascular complication of diabetes. Its pathogenesis is associated with chronic inflammation and immune res...

Neuromechanical Predictors of Clinical Scores of Balance and Functional Mobility in Chronic Stroke Survivors – A Machine Learning Approach

Clinical tests such as the Berg Balance Scale (BBS) and Timed Up and Go (TUG) are used to assess balance and functional mobility following stroke. The...