Prescriptions

Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.

To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on th...

ESG introduction mechanism of construction firms based on Bayesian network coupled with machine learning: Evidence from Zhengzhou.

Chinese construction enterprises are at a pivotal point in their transition to sustainable development, with Environmental, Social, and Governance (ESG) emerging as a key driver. However, limited understanding of ESG mechanisms hampers effective management strategies. To address this challenge, this study constructs an ESG introduction mechanism framework based on Bayesian networks and machine lea...

MSMDL-DDI: Multi-Layer Soft Mask Dual-View Learning for Drug-Drug Interactions.

Drug-drug interactions (DDIs) occur when multiple medications are co-administered, potentially leading to adverse effects and compromising patient saf...

Artificial intelligence streamlines scientific discovery of drug-target interactions.

Drug discovery is a complicated process through which new therapeutics are identified to prevent and treat specific diseases. Identification of drug-t...

Deep learning methods for proteome-scale interaction prediction.

Proteome-scale interaction prediction is essential for understanding protein functions and disease mechanisms. Traditional experimental methods are of...

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on indiv...

MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction site...

MMPD-DTA: Integrating Multi-Modal Deep Learning with Pocket-Drug Graphs for Drug-Target Binding Affinity Prediction.

Predicting drug-target binding affinity (DTA) is a crucial task in drug discovery research. Recent studies have demonstrated that pocket features and ...

Artificial intelligence in drug development.

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimen...

PPILS: Protein-protein interaction prediction with language of biological coding.

Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinfo...

An in-depth review of AI-powered advancements in cancer drug discovery.

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and e...

CPJN: News recommendation with a content and popularity joint network.

Users may click on a news because they are interested in its content or because the news contains important information and is very popular. Modeling ...

Advancements and Prospects in Nanorobotic Applications for Ophthalmic Therapy.

This study provides a bibliometric and bibliographic review of emerging applications of micro- and nanotechnology in treating ocular diseases, with a ...

An unconventional separation method of α-Terpineol from its isomer 1,8-Cineole via in situ-association formation of deep eutectic solvent and machine learning.

α-Terpineol and 1,8-cineole are two important compounds in essential oils. This study developed an efficient method to recover α-terpineol from model ...

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, eff...

Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network.

In recent years, combined drug screening has played a very important role in modern drug discovery. Generally, synergistic drug combinations are cruci...

DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.

Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the proces...

MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.

Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel d...

Haptic Shared Control Framework with Interaction Force Constraint Based on Control Barrier Function for Teleoperation.

Current teleoperated robotic systems for retinal surgery cannot effectively control subtle tool-to-tissue interaction forces. This limitation may lead...

'Applications of machine learning in liposomal formulation and development'.

Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. Th...