Prescriptions

AMP-CapsNet: a multi-view feature fusion approach for antimicrobial peptide prediction using capsule networks.

Antimicrobial peptides (AMPs) are universally found in both intracellular and extracellular settings and have significant antibiotic-resistant bacteria are becoming a bigger problem. In medical laboratories, it has shown notable anti-bacterial effectiveness in treating diabetic foot infections and related issues. New medication development frequently targets (AMPs), which are certainly ensuing com...

Introducing AutoML framework for Drug-Drug Interaction Prediction: Application of AutoGluon.

Drug-drug interactions (DDIs) remain a major concern in medication safety. Although advanced artificial intelligence methods such as deep learning have improved DDI prediction, their adoption is limited by the need for specialized expertise and complex model development. This study introduces the first application of the AutoGluon AutoML framework to DDI prediction using molecular features, aiming...

Simultaneous Segmentation of Geographic Atrophy in Longitudinally Acquired Fundus Autofluorescence Images.

PURPOSE: Accurately measuring geographic atrophy (GA) progression in clinical trials is challenging, owing to its slow and variable nature. This study...

Fully automated volumetry of ventricular subregions on computed tomography using object detection and semantic segmentation.

BACKGROUND: Our goal was to develop and validate machine learning models that are capable of fully automatic identification and segmentation of fronta...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mortality up to a year after hospital discharge. Impro...

Identification of glycosylation-related gene signatures associated with coronary artery disease via integrated transcriptomic and machine learning analyses.

BACKGROUND: On a global scale, coronary artery disease (CAD) continues to be one of the leading causes of morbidity and death. Glycosylation, a vital ...

Structure-Based Prediction of QT Prolongation Risk Using Graph Neural Networks: An Integrative Approach Combining In Vitro hERG Assays and Pharmacovigilance Data.

Drug-induced QT-interval prolongation, a non-specific biomarker of increased risk for Torsades de Pointes (TdP), is a major safety concern in drug dev...

Comparison of Artificial Intelligence Models for Automatic Segmentation of the Mandibular Canals and Branches.

OBJECTIVES: This study aimed to compare and improve the performance of three deep learning models, i.e., U-Net Transformer (UNETR), Swin UNETR, and 3D...

Classifying Delayed Dental Care Using Machine Learning: A National Health Interview Survey Analysis.

BACKGROUND: Delays in dental care worsen oral disease and mirror broader inequities in health care access and use. OBJECTIVE: To estimate the 12-month...

From black box to clear box: explainable AI for next-gen pharmacovigilance.

INTRODUCTION: Pharmacovigilance (PV) plays a vital role in post-marketing surveillance of drug safety; however, traditional methods are hindered by un...

Nanoparticle approaches for hepatitis therapy and clinical translation.

BACKGROUND: Hepatitis B and C viruses (HBV and HCV) remain major global health challenges, contributing significantly to liver-related morbidity and m...

Advancing Psychiatric Safety With the Predictive Risk Identification for Mental Health Events Tool: Retrospective Cohort Study.

BACKGROUND: Patient safety incidents are a leading cause of harm in psychiatric settings, yet early warning systems (EWS) tailored to mental health re...

Combating Antiviral Drug Resistance: A Multipronged Strategy.

ConspectusViral proteases are essential enzymes required for viral replication and assembly, making them prime antiviral drug targets. However, under ...

A Review Exploring the Translational Perspective of Artificial Intelligence in Drug Discovery and Formulation Development.

The drug development pipeline remains extraordinarily complex, costly, and time-intensive, typically requiring 10-15 years and $2-3 billion per approv...

A Multimodal Model for Caries Screening Using Intraoral Images and Questionnaires.

INTRODUCTION AND AIMS: To develop a multimodal deep learning model for dental caries screening in children by integrating intraoral photographs and qu...

Cosynllm: predicting drug combination synergy with LLM-generated descriptions.

Drug combination therapy is a well-established strategy for treating complex diseases. However, the vast combinatorial space renders exhaustive experi...

Shaping consumer behavior with artificial intelligence and brand elements.

This study is one of the few contextual and integrative empirical studies examining how artificial intelligence marketing efforts (AI MEs) can shape c...

FKSUDDAPre: A drug-disease association prediction framework based on F-TEST feature selection and AMDKSU resampling with interpretability analysis.

In drug discovery and therapeutic research, the prediction of drug-disease associations (DDAs) holds significant scientific and clinical value. Drug m...

Improving the management and outcomes of preschool wheeze: protocol of a prospective multicentre cohort study.

INTRODUCTION: Preschool wheeze and asthma are associated with substantial morbidity and impaired future lung function. Yet, wheeze is unreliably repor...

Dissociable impacts of perceived race and ascribed status in event-related brain potentials and multivariate network activity.

Humans rapidly and efficiently categorize others with limited information, forming split-second impressions. Prior EEG person perception research has ...