Prescriptions

Intelligent systems in healthcare: A systematic survey of explainable user interfaces.

With radiology shortages affecting over half of the global population, the potential of artificial intelligence to revolutionize medical diagnosis and treatment is ever more important. However, lacking trust from medical professionals hinders the widespread adoption of AI models in health sciences. Explainable AI (XAI) aims to increase trust and understanding of black box models by identifying bia...

Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes.

Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing the intricate interplay between hydrological factors, their "black box" nature makes it challenging to identify the dynamic drivers of runoff. To over...

YOLOv7-Branch: A Jujube Leaf Branch Detection Model for Agricultural Robot.

The intelligent harvesting technology for jujube leaf branches presents a novel avenue for enhancing both the quantity and quality of jujube leaf tea,...

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders.

Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have...

BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation.

Research on drug-drug interaction (DDI) prediction, particularly in identifying DDI event types, is crucial for understanding adverse drug reactions a...

Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human-Robot Interaction.

For successful human-robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to...

Adaptive self-supervised learning for sequential recommendation.

Sequential recommendation typically utilizes deep neural networks to mine rich information in interaction sequences. However, existing methods often f...

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.

Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing ...

Machine learning and explainable artificial intelligence for the prevention of waterborne cryptosporidiosis and giardiosis.

Cryptosporidium and Giardia are important parasitic protozoa due to their zoonotic potential and impact on human health, and have often caused waterbo...

A Human-AI interaction paradigm and its application to rhinocytology.

This article explores Human-Centered Artificial Intelligence (HCAI) in medical cytology, with a focus on enhancing the interaction with AI. It present...

Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction.

Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significant...

Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement.

When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor ...

Construction of a multi-tissue compound-target interaction network of Qingfei Paidu decoction in COVID-19 treatment based on deep learning and transcriptomic analysis.

The Qingfei Paidu decoction (QFPDD) is a widely acclaimed therapeutic formula employed nationwide for the clinical management of coronavirus disease 2...

Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.

Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it chal...

Machine learning in preclinical drug discovery.

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 bi...

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (Ga...

Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia.

Multiple studies within the medical field have highlighted the remarkable effectiveness of using convolutional neural networks for predicting medical ...

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recen...

In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy.

Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medication for epilepsy treatment remains an urgent and s...

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly ...