Prescriptions

Enhancing Cross-scale Feature Mutual Information via Heterogeneous Graph Contrastive Learning for Drug-Target Binding Affinity Prediction.

Predicting drug-target binding affinity is a crucial step in drug discovery, with the aim of estimating the strength of interactions between unknown drug-target pairs. In recent years, with the advancement of graph neural networks and self-supervised learning, numerous novel approaches have been proposed by researchers. However, these methods still exhibit the following drawbacks: 1) They only con...

Computer-Interpretable Domain Knowledge for Drug-Induced Acute Kidney Injury: a Knowledge Graph Approach.

In this study, a Knowledge Graph (KG) for Drug-Induced Acute Kidney Injury (DAKI) was developed to provide structured and standardized knowledge about drugs known to cause AKI. The DAKI-KG is integrated from several credible domain sources, which we standardized to international vocabularies. We demonstrate the applicability of the DAKI-KG through competency questions and expert evaluations. The e...

Transformer-based emotion recognition in interactive art: A multimodal neural approach.

Understanding how interactive digital art affects emotional states is essential to advance research into the interface between affective neuroscience ...

Machine Learning-Guided Predictive Modeling and Optimization of Polyamide-Based Thin-Film Nanocomposite Reverse Osmosis Membranes.

The growing scarcity of global freshwater resources, coupled with steady advancements in seawater desalination technology, has made the development of...

Transforming mRNA drug design with AI: From UTR and codon optimization to coordinated design.

BACKGROUND: The design of mRNA drugs involves a complex and high-dimensional optimization of sequence elements to balance stability, translation effic...

Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.

Drug induced liver toxicity remains the most common cause of acute liver failure. Conventional toxicity detection relies on resource-intensive in vivo...

Comparing supervised machine learning and large language models in title-abstract screening.

BACKGROUND: Systematic reviews require reviewers to decide on the eligibility of large numbers of articles derived from database searches. To accelera...

Generalizing Drug-Drug Interaction Risk Prediction to Chemically Novel Compounds with Bilinear Interaction Learning and Calibrated Hybrid Inference.

Reliable drug-drug interaction (DDI) prediction is essential for polypharmacy safety and for prioritizing risky combinations during early-stage drug d...

Code-free automated machine learning for OCT-based classification of vitreoretinal interface diseases.

BACKGROUND: Differentiation of vitreoretinal interface disorders on optical coherence tomography (OCT) relies on expert interpretation and can be chal...

Using Computational Intelligence to Connect Data and Drug Design in Medicinal Chemistry: A Review.

The landscape of drug discovery is being rapidly transformed by the integration of computational intelligence (CI) techniques with big data resources ...

The impact of an ambient documentation tool on the care experience of clinical pharmacists embedded in ambulatory clinics: A mixed-methods evaluation.

PURPOSE: Ambient documentation tools (ADTs) are an emerging technology designed to help clinicians complete documentation more effectively with less t...

Integrating machine learning and pharmacogenomics for biomarker discovery, identification and prioritization of potential drug candidates in ovarian cancer.

Ovarian cancer remains a major global health concern and leading cause of mortality among women due to late diagnosis, therapeutic resistance, and lim...

Artificial intelligence enabled performance evaluation of an enhanced SPR biosensor for malaria diagnosis.

Malaria remains a serious global health problem, particularly in areas where drug-resistant Plasmodium species and the expanded geographical distribut...

Emerging paradigms in nanostructured targeted drug delivery systems.

Nanostructured drug delivery systems have emerged as powerful and versatile approaches to overcome the limitations of conventional therapeutic strateg...

Utilization of Heritage Manifestations in a Reminiscence Programme (App) Directed at Older Adults with and Without Neurocognitive Disorder, Employing AI: A Pilot Study in Portugal and Spain.

Cultural and intangible heritage has been part of human daily life since time immemorial, fulfils a function within the community and acts as an eleme...

History repeats in sweet solutions: toxicological concerns and targeted recommendations following the cough syrup-linked paediatric mortalities in India.

The latest episode of cough syrup-associated pediatric deaths in India linked to the reported diethylene glycol (DEG) contamination reverberates a lon...

DataXflowGen for GenAI-driven model generation.

For years, mathematical models have been successfully used to explain biological, chemical, or physical relationships. The enormous advances in artifi...

Multimodal Artificial Intelligence Prediction of Abiraterone Efficacy in Two STAMPEDE Phase 3 Trials of Non-Metastatic Very High-Risk Prostate Cancer.

BACKGROUND: Long-term androgen deprivation therapy (LT-ADT) with radiotherapy is standard-of-care for high-risk localized prostate cancer, with abirat...

Fully Automated Free-Breathing Cardiac MRI at 3T: A Prospective Randomized Study of Image Quality, Efficiency, and Workflow Burden.

BACKGROUND: Automation in cardiac magnetic resonance (CMR) scans holds the potential to improve examination efficiency and workflow consistency. Prosp...

Performance Evaluation of GPT-5, Grok 4, and DeepSeek R1 in Interpreting Complete Blood Count Reports for Hematologic Diseases: Retrospective Comparative Study.

BACKGROUND: Large language models (LLMs) demonstrate potential in the laboratory, yet rigorous clinical evaluation remains limited. The opacity of LLM...