AIMC Topic: Machine Learning

Clear Filters Showing 31451 to 31460 of 34417 articles

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea.

Advances in experimental medicine and biology
The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this ...

.

Journal of biosciences
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational biomodelling approaches with different hybri...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNA (Shariqah, United Arab Emirates)
MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated with many diseases. Artificial Intelligence (AI) is an evolving cluster of interrelated fields, with ma...

Computational Approaches for Investigating Disease-causing Mutations in Membrane Proteins: Database Development, Analysis and Prediction.

Current topics in medicinal chemistry
Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either...

Technology Trends and Challenges for Large-Scale Scientific Visualization.

IEEE computer graphics and applications
Scientific visualization is a key approach to understanding the growing massive streams of data from scientific simulations and experiments. In this article, I review technology trends including the positive effects of Moore's law on science, the sig...

Machine learning in anesthesiology: Detecting adverse events in clinical practice.

Health informatics journal
The credibility of threshold-based alarms in anesthesia monitors is low and most of the warnings they produce are not informative. This study aims to show that Machine Learning techniques have a potential to generate meaningful alarms during general ...