Prescriptions

Comparing neural language models for medical concept representation and patient trajectory prediction.

Effective representation of medical concepts is crucial for secondary analyses of electronic health records. Neural language models have shown promise in automatically deriving medical concept representations from clinical data. However, the comparative performance of different language models for creating these empirical representations, and the extent to which they encode medical semantics, has ...

Does perceived voluntariness of others' actions induce vicarious sense of agency? Evidence from human-robot interaction.

Sense of Agency (SoA) is the feeling of control over one's actions and outcomes. People can experience "vicarious" SoA towards other agents, either other humans or artificial agents such as robots. A commonly used measure of implicit SoA is the Intentional Binding (IB) effect, which is stronger when the action is voluntary, relative to involuntary. However, it remains unclear whether this is true ...

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.

. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although frui...

Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning.

In recent years, many approved drugs have been discovered using phenotypic screening, which elaborates the exact mechanisms of action or molecular tar...

Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs.

G protein-coupled receptors (GPCRs) remain a focal point of research due to their critical roles in cell signaling and their prominence as drug target...

Applications of Artificial Intelligence in Drug Repurposing.

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the ...

A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.

Discovering therapeutic molecules requires the integration of both phenotype-based drug discovery (PDD) and target-based drug discovery (TDD). However...

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS ...

Enhancing Drug Repositioning Through Local Interactive Learning With Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGB...

Decoding Drug Response With Structurized Gridding Map-Based Cell Representation.

A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repurposing, and resistance reversal. While targeted a...

Prediction of Drug-Target Interactions With High- Quality Negative Samples and a Network-Based Deep Learning Framework.

Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared to traditional experimental methods, computer-based...

Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information.

Knowledge of unintended effects of drugs is critical in assessing the risk of treatment and in drug repurposing. Although numerous existing studies pr...

DRGCL: Drug Repositioning via Semantic-Enriched Graph Contrastive Learning.

Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technolo...

BINDTI: A Bi-Directional Intention Network for Drug-Target Interaction Identification Based on Attention Mechanisms.

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, ...

Knowledge Graph Neural Network With Spatial-Aware Capsule for Drug-Drug Interaction Prediction.

Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding ef...

Drug Repositioning via Multi-View Representation Learning With Heterogeneous Graph Neural Network.

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensiv...

RTGN: Robust Traditional Chinese Medicine Graph Networks for Patient Similarity Learning.

Traditional Chinese Medicine (TCM) boasts a long history and a unique diagnostic and therapeutic paradigm. Integrating TCM with Western medicine and m...

Multi-task interaction learning for accurate segmentation and classification of breast tumors in ultrasound images.

In breast diagnostic imaging, the morphological variability of breast tumors and the inherent ambiguity of ultrasound images pose significant challeng...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in recent years. However, the interpretation of estimate...

Harnessing machine learning for rational drug design.

A crucial part of biomedical research is drug discovery, which aims to find and create innovative medical treatments for a range of illnesses. However...