Prescriptions

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.

BACKGROUND: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection ...

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to de...

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach.

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceu...

Gating-Enhanced Hierarchical Structure Learning in Hyperbolic Space and Multi-scale Neighbor Topology Learning in Euclidean Space for Prediction of Microbe-Drug Associations.

Identifying drug-related microbes may help us explore how the microbes affect the functions of drugs by promoting or inhibiting their effects. Most pr...

A Large Language Model to Detect Negated Expressions in Radiology Reports.

Natural language processing (NLP) is crucial to extract information accurately from unstructured text to provide insights for clinical decision-making...

Prediction model of in-hospital cardiac arrest using machine learning in the early phase of hospitalization.

In hospitals, the deterioration of a patient's condition leading to death is often preceded by physiological abnormalities in the hours to days before...

Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision.

Optimized and automated methods for handling construction and demolition waste (CDW) are crucial for improving the resource recovery process in waste ...

Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically di...

Effect and optimal exercise prescription of robot-assisted gait training on lower extremity motor function in stroke patients: a network meta-analysis.

OBJECTIVE: This study aimed to evaluate the effectiveness of robot-assisted gait training (RAGT) and explore the optimal exercise prescription using a...

AI-induced indifference: Unfair AI reduces prosociality.

The growing prevalence of artificial intelligence (AI) in our lives has brought the impact of AI-based decisions on human judgments to the forefront o...

Automated linguistic analysis in youth at clinical high risk for psychosis.

Identifying individuals at clinical high risk for psychosis (CHRP) is crucial for preventing psychosis and improving the prognosis for schizophrenia. ...

Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking.

The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystall...

AI in obstetrics: Evaluating residents' capabilities and interaction strategies with ChatGPT.

In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assess...

Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. A...

Multitype view of knowledge contrastive learning for recommendation.

Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, ...

Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.

Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from re...

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion ar...

A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis.

Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a ch...

Magnetic Miniature Soft Robot with Reprogrammable Drug-Dispensing Functionalities: Toward Advanced Targeted Combination Therapy.

Miniature robots are untethered actuators, which have great prospects to transform targeted drug delivery because they can potentially deliver high co...

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after...