Prescriptions

Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.

The integration of multiomics technologies with artificial intelligence (AI) has become a transformative force in modern precision medicine, particularly within drug discovery. Multiomics approaches, including genome-wide association studies, transcriptomic profiling, proteomic interaction mapping, and metabolomic sequencing, provide unparalleled insights into the molecular dynamics of disease pat...

Cross-study interpretable machine learning for prediction of NH3 and H2S emissions and identification of operational windows during composting.

Accurate prediction of NH3 and H2S emissions is essential for odor-risk control and process management during composting, but remains challenging because emissions are driven by nonlinear, stage-dependent, and interacting operating conditions. In this study, we developed an interpretable cross-study machine-learning framework for NH3 and H2S prediction using routinely monitored composting variable...

Complex in vitro model developers' perspective on advanced hepatic in vitro culture methods for predictive drug safety.

INTRODUCTION: Adverse drug reactions remain a major barrier to drug development, with hepatotoxicity representing a persistent cause of clinical failu...

Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.

Predicting drug-induced cardiotoxicity remains one of the most important challenges in drug safety, contributing to a substantial share of clinical tr...

Learning Human-Environment Interactions via Wearable AI Interfaces.

Wearable artificial intelligence (AI) interfaces are reshaping the boundaries between humans and the environment. While prior works often focus on nar...

Global-Local Interaction and Recalibration Network for Salient Object Detection in Optical Remote Sensing Images.

Optical remote sensing images (RSIs) exhibit extensive spatial coverage and complex geographic backgrounds, where salient objects in the optical RSIs ...

IHGCN-PLA: An interpretable heterogeneous graph convolutional network for protein-ligand binding affinity prediction with multimodal interaction fusion.

Protein-ligand binding affinity prediction is fundamental to computer-aided drug discovery, enabling accelerated therapeutic development at reduced co...

FSSM-DDI: Fusion State Space Model for predicting drug-drug interaction using social-media and drug descriptions.

BACKGROUND: Predicting drug-drug interactions (DDIs) from social-media and drug descriptions is crucial for healthcare, drug regulation, and pharmaceu...

Precision Medicine Gene Network Analyser: part I-cancer driver gene identification through network topology and ensemble machine learning.

PURPOSE: Precision oncology depends on identifying cancer driver genes and linking them to targeted therapies. Current methods using curated gene sets...

Ontology-Enhanced Deep Learning for Mechanistic Prediction of Drug-Drug Interactions: A Clinically Interpretable Framework.

Drug-drug interactions (DDIs) have critical impacts on patient safety and healthcare efficiency because of their significant contributions to adverse ...

HKD-CPI: high-order knowledge distillation enhanced inductive compound-protein interaction prediction.

MOTIVATION: Accurately identifying compound-protein interactions (CPIs) is critical for accelerating drug discovery. Recent deep learning methods have...

Reproducibility, validation, and failure modes across classical and AI-driven molecular docking.

Molecular docking is indispensable across computer‑aided discovery. However, its conclusions often hinge more on modeling choices than on software bra...

Human and Robot Assistance for Cognitive Load in Younger and Older Adults: Multimodal Within-Subject Experimental Study.

BACKGROUND: Maintaining cognitive efficiency and independence is a central goal of healthy aging. Socially assistive robots (SARs) are increasingly pr...

CM-MTL-DTI: Drug-Target Interaction Prediction via Cross-Modal Alignment and Multi-Task Learning.

Drug-target interaction (DTI) prediction is critical for candidate compound screening and elucidation of mechanisms of action in drug discovery and re...

From Parasite to Pill: Harnessing Biology for Breakthroughs in Antimalarial Drug Discovery.

The global imperative for malaria eradication demands innovative strategies for antimalarial drug discovery, particularly in the face of growing drug ...

Carfentanil in the illicit drug supply: A machine learning approach for trace detection using surface-enhanced Raman spectroscopy.

BACKGROUND: Carfentanil is an extremely potent synthetic fentanyl analogue often present at trace levels alongside other fentanyl analogues and long-a...

Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.

Radial artery puncture, a routine arterial cannulation procedure for perioperative and critical care settings, is limited by high first-attempt failur...

A critical evaluation of generative query expansion on biomedical literature retrieval.

OBJECTIVE: To evaluate the effectiveness of generative query expansion for biomedical literature retrieval. MATERIALS AND METHODS: We thoroughly exami...

Predicting pharmacy choice for managed care network design.

BACKGROUND: Pharmacy type selection is a key component of medication access and use. Prior studies have commonly used logistic regression to examine p...

Machine learning evaluation of racial and ethnic inclusion under alternative cost-based approaches to Medicare Medication Therapy Management eligibility.

BACKGROUND: The eligibility framework for the Medicare Medication Therapy Management (MTM) program has been associated with a lower likelihood of meet...