AIMC Topic: Lipids
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Using isotopic envelopes and neural decision tree-based in silico fractionation for biomolecule classification.
Analytica chimica acta
Feb 20, 2020
Untargeted mass spectrometry (MS) workflows are more suitable than targeted workflows for high throughput characterization of complex biological samples. However, analysis workflows for untargeted methods are inadequate for characterization of comple...
Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.
European journal of preventive cardiology
Feb 4, 2020
AIMS: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores - for example, the Dut...
Systematic Modeling of log Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.
Journal of chemical information and modeling
Jan 10, 2020
Lipophilicity, as evaluated by the -octanol/buffer solution distribution coefficient at pH = 7.4 (log ), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In thi...
Nutritional and technological properties of a quinoa (Chenopodium quinoa Willd.) spray-dried powdered extract.
Food research international (Ottawa, Ont.)
Dec 7, 2019
The relevance of an appropriate nutrition requires innovation in the design of food ingredients. The goal of this work was to obtain a powdered extract of quinoa by using spray-drying. To this aim, quinoa flour was suspended in water to obtain a solu...
Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.
Metabolism: clinical and experimental
Nov 9, 2019
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirr...
Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.
Journal of chemical theory and computation
Oct 7, 2019
Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that ar...
Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation.
Physical biology
Jul 22, 2019
Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free q...
Identification of the lipid-lowering component of triterpenes from Alismatis rhizoma based on the MRM-based characteristic chemical profiles and support vector machine model.
Analytical and bioanalytical chemistry
May 14, 2019
It has been demonstrated that triterpenes in Alismatis rhizoma (Zexie in Chinese, ZX) contributed to the lipid-lowering effect on high-fat diet-induced hyperlipidemia. Alisol B 23-acetate, one of the abundant triterpenes in ZX, was used as the marker...
Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.
Diabetes technology & therapeutics
Apr 10, 2019
The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U...
Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.
Magnetic resonance imaging
Apr 5, 2019
PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque c...
Reproducibility of Results
Aged
Sensitivity and Specificity
Male
Middle Aged
Contrast Media
Calcinosis
Lipids
Humans
Machine Learning
Algorithms
Female
Plaque, Amyloid
Plaque, Atherosclerotic
Angiography
Necrosis
Carotid Stenosis
Cerebral Hemorrhage
Bayes Theorem
Carotid Arteries
Carotid Artery, Common
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