AIMC Topic: Machine Learning

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CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs).

Cancer detection and classification using a simplified binary state vector machine.

Medical & biological engineering & computing
Cancer is an invasive and malignant growth of cells and is known to be one of the most fatal diseases. Its early detection is essential for decreasing the mortality rate and increasing the probability of survival. This study presents an efficient mac...

Understanding ChatGPT for evidence-based utilization in interventional radiology.

Clinical imaging
Advancement in artificial intelligence (AI) has the potential to improve the efficiency and accuracy of medical care. New techniques used in machine learning have enhanced the functionality of software to perform advanced tasks with human-like capabi...

Explaining protein-protein interactions with knowledge graph-based semantic similarity.

Computers in biology and medicine
The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of...

Pretraining Strategies for Structure Agnostic Material Property Prediction.

Journal of chemical information and modeling
In recent years, machine learning (ML), especially graph neural network (GNN) models, has been successfully used for fast and accurate prediction of material properties. However, most ML models rely on relaxed crystal structures to develop descriptor...

Learning the shape of protein microenvironments with a holographic convolutional neural network.

Proceedings of the National Academy of Sciences of the United States of America
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or struct...

Deep Learning Algorithm Based on Molecular Fingerprint for Prediction of Drug-Induced Liver Injury.

Toxicology
Drug-induced liver injury (DILI) is one the rare adverse drug reaction (ADR) and multifactorial endpoints. Current preclinical animal models struggle to anticipate it, and in silico methods have emerged as a way with significant potential for doing s...

A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.

Scientific data
Urinary tract infection (UTI) is a common disorder. Its diagnosis can be made by microscopic examination of voided urine for markers of infection. This manual technique is technically difficult, time-consuming and prone to inter-observer errors. The ...