Prescriptions

How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.

IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) tria...

extract in combination with lytic phage cocktails: a promising therapeutic approach against biofilms of multi-drug resistant .

Antimicrobial resistance (AMR) poses a significant global threat to public health systems, rendering...

Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.

PURPOSE: Drug-induced liver injury, or DILI, affects numerous patients and also presents significant...

Child-Centric Robot Dialogue Systems: Fine-Tuning Large Language Models for Better Utterance Understanding and Interaction.

Dialogue systems must understand children's utterance intentions by considering their unique linguis...

The native Iranian soil bacteria with high potential to produce extracellular methionine gamma-lyase.

This study aimed to screen native methionine gamma-lyase (L-methioninase) producing bacteria from so...

Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques.

 The aim of this study was to explore an innovative approach for developing deep learning (DL) algo...

Improving binding affinity prediction by emphasizing local features of drug and protein.

Binding affinity prediction has been considered as a fundamental task in drug discovery. Despite muc...

Editorial: Spotlighting the interaction network of hub genes, molecules, and cells in the tumor immune microenvironment (TIME) and their contribution to malignant progression.

[Current status and outlooks of acupuncture research driven by machine learning].

The machine learning is used increasingly and widely in acupuncture prescription optimization, intel...

De Novo Drug Design by Multi-Objective Path Consistency Learning With Beam A Search.

Generating high-quality and drug-like molecules from scratch within the expansive chemical space pre...

A Knowledge Graph-Based Method for Drug-Drug Interaction Prediction With Contrastive Learning.

Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and ...

A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction.

Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especiall...

Reinforced Metapath Optimization in Heterogeneous Information Networks for Drug-Target Interaction Prediction.

Graph neural networks offer an effective avenue for predicting drug-target interactions. In this dom...

MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identi...

KGRLFF: Detecting Drug-Drug Interactions Based on Knowledge Graph Representation Learning and Feature Fusion.

Accurate prediction of drug-drug interactions (DDIs) plays an important role in improving the effici...

HGLA: Biomolecular Interaction Prediction Based on Mixed High-Order Graph Convolution With Filter Network via LSTM and Channel Attention.

Predicting biomolecular interactions is significant for understanding biological systems. Most exist...

SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification.

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There...

RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.

Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the ...

Unravelling metabolite-microbiome interactions in inflammatory bowel disease through AI and interaction-based modelling.

Inflammatory Bowel Diseases (IBDs) are chronic inflammatory disorders of the gastrointestinal tract ...

Combined effect of Tetracycline compounds and essential oils on antimicrobial resistant isolated from the swine food chain.

Antimicrobial resistance (AMR) poses risks for food stakeholders because of the spread of resistant ...

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.

Accurately predicting drug response depending on a patient's genomic profile is critical for advanci...