Prescriptions

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. N...

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpret...

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmac...

Real-world artificial intelligence-based interpretation of fundus imaging as part of an eyewear prescription renewal protocol.

OBJECTIVE: A real-world evaluation of the diagnostic accuracy of the Opthai® software for artificial intelligence-based detection of fundus image abno...

Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.

Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular librar...

Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction.

Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based metho...

Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets.

OBJECTIVE: The primary objective of this review is to investigate the effectiveness of machine learning and deep learning methodologies in the context...

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning.

Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropic spatially-variant deformations in the reconstruc...

HormoNet: a deep learning approach for hormone-drug interaction prediction.

Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormon...

Severe Hyperkalemia During a Robot-Assisted Total Radical Prostatectomy in a Patient with Stage 3a Chronic Kidney Disease: A Case Report.

A 63-year-old man with stage 3a chronic kidney disease (CKD) and mild hyperkalemia was scheduled for a robot-assisted prostatectomy. He was being trea...

AI-enhanced biomedical micro/nanorobots in microfluidics.

Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundame...

Exploring the Use of Socially Assistive Robots Among Socially Isolated Korean American Older Adults.

This pilot study explored whether a socially assistive robot (SAR) would have positive effects on Korean American immigrant older adults' health behav...

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical studies demonstrated age-related changes in human...

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the ...

A stroke prediction framework using explainable ensemble learning.

The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut off, resulting in a stroke. Early recognition of str...

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.

Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. Howev...

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.

Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) meth...

Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning.

In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the development of oral drugs with greater bioavailabi...

Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects.

Medicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties...

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.

Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs...