Prescriptions

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.

The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficu...

Bimodal Intelligent Electronic Skin Based on Proximity and Tactile Interaction for Pressure and Configuration Perception.

The flexible bimodal e-skin exhibits significant promise for integration into the next iteration of human-computer interactions, owing to the integration of tactile and proximity perception. However, those challenges, such as low tactile sensitivity, complex fabrication processes, and incompatibility with bimodal interactions, have restricted the widespread adoption of bimodal e-skin. Herein, a bi...

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.

. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment whe...

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations.

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have d...

Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma.

Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual respo...

TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics.

Cellular communication relies on the intricate interplay of signaling molecules, forming the Cell-cell Interaction network (CCI) that coordinates tiss...

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery proc...

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to...

Comparison of proactive and reactive interaction modes in a mobile robotic telecare study.

Mobile robotic telepresence systems require that information about the environment, the task, and the robot be presented to a remotely located user (o...

Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms.

BACKGROUND: Preventable patient harm, particularly medication errors, represent significant challenges in healthcare settings. Dispensing the wrong me...

AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. W...

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biologic...

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. M...

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...

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...