Prescriptions

Genetic algorithm-optimized artificial neural network for multi-objective optimization of biomass and exopolysaccharide production by Haloferax mediterranei.

Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components (i.e., sucrose, yeast extract, and urea) were optimized for biomass growth and extracellular EPS production in Haloferax mediterranei DSM 1411 using Box-Behnken design. In a multi-objective optimization framework, response surface methodology (RSM) a...

Drug-drug interaction prediction based on graph contrastive learning and dual-view fusion.

Drug-drug interaction (DDI) is important in drug research and are one of the major causes of morbidity and mortality. The deep learning methods can automatically extract drug features from molecular graphs or drug-related networks, which improves the performance of DDI prediction. However, there is noise and incomplete data in existing datasets, and the volume of dataset is limited. In order to fu...

Functional Imaging of CYP3A4 at Multiple Dimensions Using an AI-Driven High Performance Fluorogenic Substrate.

Cytochrome P450 3A4 (CYP3A4) is a key mediator in xenobiotic metabolism and drug-drug interactions (DDI), developing orally active fluorogenic substra...

Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD)...

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like ...

A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.

Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This...

Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks.

Efficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image sign...

Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.

Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, cal...

Artificial intelligence in anti-obesity drug discovery: unlocking next-generation therapeutics.

Obesity, a multifactorial disease linked to severe health risks, requires innovative treatments beyond lifestyle changes and current medications. Exis...

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through e...

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.

Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. Levothyroxine (LT4) is the standard therapy; howeve...

Molecular Generation for CNS Drug Discovery and Design.

Computational drug design is a rapidly evolving field, especially the latest breakthroughs in generative artificial intelligence (GenAI) to create new...

Protein-ligand interaction prediction based on heterogeneity maps and data enhancement.

Prediction of protein-ligand interactions is critical for drug discovery and repositioning. Traditional prediction methods are computationally intensi...

Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning.

The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multife...

Social robots as conversational catalysts: Enhancing long-term human-human interaction at home.

The integration of social robots into family environments raises critical questions about their long-term influence on family interactions. This study...

Application of the LDA model to identify topics in telemedicine conversations on the X social network.

The evolution experienced by global society, in the post-COVID 19 era, is marked by the quite obligatory use of digital media in many sectors, as is t...

GramSeq-DTA: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information.

Drug-target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accura...

An Optimised Mobilenet V2 Attention Parallel Network for Predicting Drug-Drug Interactions Through Combining Local and Global Features.

Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effe...

MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug-Drug Interaction Prediction.

Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the ch...

Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.

To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy ...