AIMC Topic: Algorithms

Clear Filters Showing 4381 to 4390 of 28713 articles

Simple and effective embedding model for single-cell biology built from ChatGPT.

Nature biomedical engineering
Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatG...

Advancements in exponential synchronization and encryption techniques: Quaternion-Valued Artificial Neural Networks with two-sided coefficients.

Neural networks : the official journal of the International Neural Network Society
This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued Artificial Neural Networks (QVANNs) that incorporate two-sided coefficients. The study introduces a novel approach t...

DropNaE: Alleviating irregularity for large-scale graph representation learning.

Neural networks : the official journal of the International Neural Network Society
Large-scale graphs are prevalent in various real-world scenarios and can be effectively processed using Graph Neural Networks (GNNs) on GPUs to derive meaningful representations. However, the inherent irregularity found in real-world graphs poses cha...

Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.

International journal of medical informatics
OBJECTIVE: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end...

Tunable and real-time automatic interventional x-ray collimation from semi-supervised deep feature extraction.

Medical physics
BACKGROUND: The use of endovascular procedures is becoming increasingly popular across multiple clinical domains. These procedures are generally performed under image guidance using an interventional c-arm x-ray system. Radiation exposure to both pat...

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

Journal of computational biology : a journal of computational molecular cell biology
Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have be...

AI-driven feature selection and epigenetic pattern analysis: A screening strategy of CpGs validated by pyrosequencing for body fluid identification.

Forensic science international
Identification of body fluid stain at crime scene is one of the important tasks of forensic evidence analysis. Currently, body fluid-specific CpGs detected by DNA methylation microarray screening, have been widely studied for forensic body fluid iden...

Current status and future directions of explainable artificial intelligence in medical imaging.

European journal of radiology
The inherent "black box" nature of AI algorithms presents a substantial barrier to the widespread adoption of the technology in clinical settings, leading to a lack of trust among users. This review begins by examining the foundational stages involve...

Protein-protein interaction detection using deep learning: A survey, comparative analysis, and experimental evaluation.

Computers in biology and medicine
This survey paper provides a comprehensive analysis of various Deep Learning (DL) techniques and algorithms for detecting protein-protein interactions (PPIs). It examines the scalability, interpretability, accuracy, and efficiency of each technique, ...

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.

Advances in therapy
Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) res...