Prescriptions

The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence.

This work aims to enhance the accuracy and efficiency of corporate strategic decision-making, particularly in rapidly changing and highly competitive market environments. Traditional strategic decision-making methods rely on managers' experiential judgment and exhibit limitations when handling complex data and high-frequency market fluctuations. To address this issue, this work proposes a hybrid o...

WDGBANDTI: A Deep Graph Convolutional Network-Based Bilinear Attention Network for Drug-Target Interaction Prediction with Domain Adaptation.

BACKGROUNDS: During the development of new drugs, it is essential to assess their effectiveness and examine the potential mechanisms behind side effects. This process typically involves combining the analysis of drugs under development with relevant existing drugs to more precisely evaluate the effects of drugs and targets. The use of deep learning methods to analyze this problem is currently a re...

Cortical myelin mapping in antipsychotic medication-naïve, first-episode psychosis patients.

While white matter myelin primarily functions to accelerate conduction velocity and has been extensively studied in schizophrenia-spectrum disorders (...

Optimizing Treatment for Depression in Primary Care Using Psychotherapy Versus Antidepressant Medication in a Low-Resource Setting: Protocol for the OptimizeD Randomized Controlled Trial.

Psychotherapy and antidepressant medications are first-line treatments for depression, and they both have significant treatment effects on average. H...

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) pl...

Machine Learning-Based Analysis of Differentially Expressed Genes in the Muscle Transcriptome Between Beef Cattle and Dairy Cattle.

Muscle is a crucial component of cattle, playing a vital role in determining the final quality of beef. This study aimed to identify candidate genes a...

Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening.

In the drug discovery process, traditional structure-based virtual screening methods, such as molecular docking, are often limited by empirical scorin...

The silent epidemic of non-adherence - insights from the 2024 a:care congress.

Medication non-adherence is a global challenge with profound implications for patient health outcomes, healthcare systems, and society at large. This ...

Antiswelling, Ultrastretchable, and Ultrastable Hydrogel Sensors for Long-Term Underwater Monitoring.

Wearable strain sensors designed for underwater environments and monitoring users' health status and safety are highly desirable. However, constructin...

Machine learning models for pharmacogenomic variant effect predictions - recent developments and future frontiers.

Pharmacogenomic variations in genes involved in drug disposition and in drug targets is a major determinant of inter-individual differences in drug re...

Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Dr...

Ultrasound Diagnosis of Pelvic Organ Prolapse Using Artificial Intelligence.

The aim of this study was to design a fully automated hybrid AI-based method, combining a convolutional neural network (CNN) and a tree-based model (...

DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals.

Accurate characterization of cellular states is the foundation for precise prediction of drug sensitivity in cancer cell lines, which in turn is funda...

Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices.

Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans....

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity.

Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced...

Sign-aware Graph Contrastive Learning for Drug Repositioning.

Drug repositioning, which identifies new therapeutic potential of approved drugs, is pivotal in accelerating drug discovery. Recently, growing efforts...

Knowledge Spillover, Trust, Effort, and Error Exposure in Peer-Assisted Learning.

Peer-assisted learning has the potential to improve learning in academic settings and beyond. However, the cognitive and motivational mechanisms of le...

Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.

Prostate cancer is the most prevalent solid tumor in males and one of the most common causes of male mortality. It is the most common type of cancer i...

Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis.

Understanding the interaction between T Cell Receptors (TCRs) and peptide-bound Major Histocompatibility Complexes (pMHCs) is crucial for comprehendin...

Development of a warning model for drug-induced liver injury in the older patients.

INTRODUCTION: Drug-induced liver injury (DILI) is a significant adverse drug reaction, ranging from mild liver enzyme elevations to severe outcomes su...