Prescriptions

Usability and Recall Evaluation of Virtual Reality Ontology Object Manipulation (VROOM) System.

Biomedical ontologies are repositories of knowledge that encapsulate biomedical terms and the relationships between them. When visualized, ontologies are complex graphs, where each node represents one biomedical concept, and links express binary relationships between pairs of concepts. Such a network can have thousands of nodes, making visualization and manipulation difficult. This paper presents ...

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides ...

Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis.

Bladder cancer is the most common urological malignancy worldwide, and its high recurrence rate leads to poor survival outcomes. The effect of antican...

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at e...

The sex effect on balance control while standing on vestibular-demanding tasks with/without vestibular simulations: implication for sensorimotor training for future space missions.

Anatomical differences between sexes in the vestibular system have been reported. It has also been demonstrated that there is a sex difference in bal...

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, variou...

The role of artificial intelligence in informed patient consent for radiotherapy treatments-a case report.

Recent advancements in large language models (LMM; e.g., ChatGPT (OpenAI, San Francisco, California, USA)) have seen widespread use in various fields,...

Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks.

Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots...

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is ...

Targeting ion channels with ultra-large library screening for hit discovery.

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in dise...

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Dev...

Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis.

STATEMENT OF PROBLEM: With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in impl...

Control list of high-priority chemicals based on 5-HT-RI functionality and the human health interference effects selective CNN-GRU deep learning model.

The antidepressant drug known as 5-HT reuptake inhibitor (5-HT-RI) was commonly detected in biological tissues and result in significant adverse healt...

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-d...

PEB-DDI: A Task-Specific Dual-View Substructural Learning Framework for Drug-Drug Interaction Prediction.

Adverse drug-drug interactions (DDIs) pose potential risks in polypharmacy due to unknown physicochemical incompatibilities between co-administered dr...

Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China).

The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, r...

The emergence of machine learning force fields in drug design.

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental...

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster a...

AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions.

PURPOSE: Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treat...

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network.

Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficia...