AIMC Topic: Machine Learning

Clear Filters Showing 20881 to 20890 of 34417 articles

Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a).

International journal of molecular sciences
Plasma lipoproteins are important carriers of cholesterol and have been linked strongly to cardiovascular disease (CVD). Our study aimed to achieve fine-grained measurements of lipoprotein subpopulations such as low-density lipoprotein (LDL), lipopro...

Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods.

International journal of molecular sciences
Atherosclerosis is the underlying pathology in a major part of cardiovascular disease, the leading cause of mortality in developed countries. The infiltration of monocytes into the vessel walls of large arteries is a key denominator of atherogenesis,...

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics.

Scientific reports
One of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug d...

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

Applied clinical informatics
BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictiv...

Multi-dimensional predictions of psychotic symptoms via machine learning.

Human brain mapping
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have pr...

L-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification.

Computational biology and chemistry
With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein info...

Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients.

Journal of chemical information and modeling
Computational prediction of limiting activity coefficients is of great relevance for process design. For highly nonideal mixtures including molecules with directed interactions, methods that maintain the molecular character of the solvent are most pr...