AIMC Topic: Machine Learning

Clear Filters Showing 30731 to 30740 of 34417 articles

Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity.

Mathematical biosciences and engineering : MBE
Predicting the risk of mortality of hospitalized patients in the ICU is essential for timely identification of high-risk patients and formulate and adjustment of treatment strategies when patients are hospitalized. Traditional machine learning method...

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Briefings in bioinformatics
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gain...

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders.

Briefings in bioinformatics
Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in...

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Briefings in bioinformatics
Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are consid...

Rapid Determination of Polysaccharides in Cistanche Tubulosa Using Near-Infrared Spectroscopy Combined with Machine Learning.

Journal of AOAC International
BACKGROUND: Cistanche tubulosa, as a homology of medicine and food, not only has a unique medicinal value but also is widely used in healthcare products. Polysaccharide is one of its important quality indicators.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

The Journal of chemical physics
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fi...