Prescriptions

Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia worldwide, with catheter ablation being an effective yet recurrence-prone treatment. Given the limited accuracy of conventional risk scores in identifying patients at high risk of recurrence after catheter ablation, this study sought to develop and validate a machine learning (ML) model for predicting AF recurrence using a wide arr...

CADS: A Causal Inference Framework for Identifying Essential Genes to Enhance Drug Synergy Prediction.

MOTIVATION: Drug synergy is crucial for developing effective combination therapies, but traditional screening methods suffer from inefficiency and high costs. While deep learning shows promise for predicting drug synergy, current approaches using Transformers and graph neural networks focus on combining drug and cell line features without modelling how genes causally influence drug responses. RESU...

Exploring the sustainable packaging sector through European companies' LinkedIn profiles with unsupervised machine learning.

The rapidly increasing demand for goods and services has led to increased use of packaging for products. Production and consumption practices are crit...

Stem Cell-Derived Organoids as a Next-Generation Platform for Drug Toxicity and Efficacy Testing: Toward Replacing Animal Models in Regulatory Science.

Three-dimensional, self-organizing structures derived from stem cells, known as organoids, represent a groundbreaking advancement in preclinical drug ...

The gut microbiota dysbiosis in geriatric multimorbidity: Pharmacotherapeutic implications, pathophysiological mechanisms, and precision modulation strategies.

Aging around the world is accelerating. With that comes the intersection of geriatric multimorbidity and polypharmacy, creating a large uncertainty ab...

ProtFPreDTI: Drug-Target Interaction Prediction Study and LIME Interpretability Analysis Based on the ProtBERT Deep Language Model with Adaptive Fuzzy Sampling Strategy.

The interaction mechanism between drugs and targets is a core topic of modern pharmaceutical research, and its precise analysis can not only significa...

RLBindDeep: A ResNet-LSTM based novel framework for protein-ligand binding affinity prediction.

The prediction of the binding affinity of proteins and ligands in computational drug discovery with high accuracy is critical when evaluating the effe...

FG-DDI: Functional group-aware graph neural networks for drug-drug interaction prediction.

OBJECTIVE: We aim to improve Drug-Drug Interactions (DDIs) by explicitly injecting medicinal-chemistry knowledge of functional groups (FGs) into graph...

Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

Predicting cytochrome P450 (CYP450) ligand binding is critical in early-stage drug discovery as CYP450-mediated metabolism profoundly influences drug ...

Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.

OBJECTIVE: To compare AI-augmented and conventional double reading in organised breast-cancer screening with respect to cancer-detection rate (CDR), r...

Utilizing artificial intelligence for the diagnosis of ocular surface squamous neoplasia with ultrasound biomicroscopy images.

PURPOSE: This study aims to develop an artificial intelligence (AI) model to assist ophthalmologists in distinguishing ocular surface squamous neoplas...

Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing.

OBJECTIVE: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome an...

Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.

The accurate prediction of drug-induced side effects remains a significant challenge in pharmaceutical development, particularly in early development,...

Artificial intelligence and machine learning in antimicrobial discovery, resistance prediction, and precision therapy.

OBJECTIVES: The global crisis of antimicrobial resistance (AMR) demands a paradigm shift in traditional drug discovery, and artificial intelligence (A...

Psychological factors in consumer intentions toward smartwatches: A study on technology adoption and behavior.

This research examines the psychological factors that contribute to consumer intentions toward smartwatches, with a particular focus on technology ado...

A multi-layer hypergraph framework for drug-drug interaction prediction based on transformer and hypergraph convolution.

Drug-drug interactions represent a key problem for drug research, development, and clinical practice. It is crucial to accurately predict interactions...

A BMC-Net model for the recognition and segmentation of mandibular canal bifurcation.

OBJECTIVES: To accurately identify the anatomical structure and trajectory of the mandibular canal, helping dentists avoid surgical risks and develop ...

Prediction of Intravenous Pharmacokinetic Parameters across Multiple Species by a Multifidelity Deep Learning Framework.

Prediction of pharmacokinetic (PK) properties is essential for early drug candidate screening and dosage regimen optimization. In recent years, using ...

Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Peroxisome proliferator-activated receptor γ (PPARγ) is a key therapeutic target for type 2 diabetes and cardiovascular diseases due to its central ro...

Altered gut microbiome function in ADHD: More Prevotella, less vitamin B12 biosynthesis, and beneficial modulation by synbiotic treatment.

The effect of psychostimulant medication in ADHD on the gut microbiome remains unknown. Oral Synbiotic 2000, comprising multiple lactic acid bacteria ...