Obesity

Estimation of Food Intake Quantity Using Inertial Signals from Smartwatches

Accurate monitoring of eating behavior is crucial for managing obesity and eating disorders such as bulimia nervosa. At the same time, existing methods rely on multiple and/or specialized sensors, greatly harming adherence and ultimately, the quality and continuity of data. This paper introduces a novel approach for estimating the weight of a bite, from a commercial smartwatch. Our publicly-avai...

MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

Recent advances in Retrieval-Augmented Generation (RAG) have significantly improved response accuracy and relevance by incorporating external knowledge into Large Language Models (LLMs). However, existing RAG methods primarily focus on generating text-only answers, even in Multimodal Retrieval-Augmented Generation (MRAG) scenarios, where multimodal elements are retrieved to assist in generating ...

Stochastic Linear Bandits with Latent Heterogeneity

This paper addresses the critical challenge of latent heterogeneity in online decision-making, where individual responses to business actions vary d...

A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran

Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary A...

Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence

Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a ...

Discrimination and AI in insurance: what do people find fair? Results from a survey

Two modern trends in insurance are data-intensive underwriting and behavior-based insurance. Data-intensive underwriting means that insurers use and...

The Effect of Covid-19 Lockdown on Human Behaviour Using Analytical Hierarchy Process

The coronavirus pandemic corresponds to a serious global health crisis which not only changed the way people used to live but also how people behave...

Unraveling the Co-Morbidity between COVID-19 and Neurodegenerative Diseases Through Multi-scale Graph Analysis: A Systematic Investigation of Biological Databases and Text Mining

The COVID-19 pandemic has generated a vast volume of research, yet much of it focuses on individual diseases, overlooking complex comorbidity relation...

Matrix effects influence biochemical signatures and metabolite quantification in dried blood spots

Dried blood spots (DBS) represent a convenient clinical sample material, offering low infection risk, easy transport, and long-term metabolite stabili...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Kno...

DeepDiff-SHAP: Interpretable deep learning for subgroup-specific causal inference using conditional SHAP

Precision medicine aims to tailor healthcare strategies to individual differences in genetic, clinical, and environmental factors. However, identifyin...

Benchmarking large language models for cell-free RNA diagnostic biomarker discovery

Large-language models (LLMs) can parse vast amounts of data and generate executable code, positioning them as promising tools for the development of b...

Satiation is associated with OGT-dependent regulation of excitatory synapses

Satiation is essential for energy homeostasis and is dysregulated in metabolic disorders like obesity and eating disorders such as anorexia nervosa. W...

Fourier transform infrared spectroscopy enables rapid species discrimination across Malassezia and strain-level typing in M. pachydermatis

Malassezia pachydermatis is a zoophilic yeast found on the skin and in the outer ear canal of many mammals. It normally maintains a commensal lifestyl...

Automated generation of personalized trajectories of aging phenotypes with DyViA-GAN

With a general increase in human lifespan, the need for technological advances to develop strategies for healthy aging has assumed great importance. I...

PhageAI: a new approach to predicting the lifestyle of bacteriophages using proteinBERT and convolutional neural networks

Bacteriophages are viruses that infect bacteria, including temperate, virulent and chronic phages. In the current times of increasing resistance to an...

Predicting Clinical Outcomes in Helicobacter pylori-positive Patients using Supervised Learning through the Integration of Demographic and Genomic Features

Helicobacter pylori (H. pylori) infection is widespread globally and is linked to outcomes ranging from chronic gastritis to gastric cancer. However, ...

VirTrack: A Framework for Inferring Viral Influence on Disease-Associated Transcriptomes — Clinical Type-Specific Epstein–Barr Virus Pathogenesis in Multiple Sclerosis

Epstein–Barr virus (EBV) is strongly implicated in Multiple Sclerosis (MS), but how its influence varies across MS clinical types remains unclear. We ...

MAP-PRS: Multi-Ancestry Portfolio-Based Polygenic Risk Scores

Polygenic Risk Scores (PRS) are emerging tools for predicting an individual’s genetic risk for complex diseases. However, their usefulness in clinical...

Deep learning models reading clinical data and liver omics strongly distinguish NASH from steatosis and suggest new genes involved in liver disease severity

Metabolic dysfunction-associated steatotic liver disease (MASLD, previously NAFLD) is a frequent co-morbidity of obesity and diabetes, with prevalence...