AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Metabolomics

Showing 221 to 230 of 298 articles

Clear Filters

Application of ensemble deep neural network to metabolomics studies.

Analytica chimica acta
Deep neural network (DNN) is a useful machine learning approach, although its applicability to metabolomics studies has rarely been explored. Here we describe the development of an ensemble DNN (EDNN) algorithm and its applicability to metabolomics s...

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Scientific reports
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress...

Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning.

Analytica chimica acta
Large amounts of data are generally produced from mass spectrometry imaging (MSI) experiments in obtaining the molecular and spatial information of biological samples. Traditionally, MS images are constructed using manually selected ions, and it is v...

Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

Scientific reports
Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing...

Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables.

Analytical chemistry
Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because the...

Gas chromatography-mass spectrometry metabolomic study of lipopolysaccharides toxicity on rat basophilic leukemia cells.

Chemico-biological interactions
Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production, fatal sepsis syndrome and depression/multiple organ failure, as pathophysiologic demonstration. Various toxic effects of LPS have been extensively reported, mainly on the toxici...

Exhaled breath condensate metabolome clusters for endotype discovery in asthma.

Journal of translational medicine
BACKGROUND: Asthma is a complex, heterogeneous disorder with similar presenting symptoms but with varying underlying pathologies. Exhaled breath condensate (EBC) is a relatively unexplored matrix which reflects the signatures of respiratory epitheliu...

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Journal of proteome research
Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it re...

Environmental metabolomics with data science for investigating ecosystem homeostasis.

Progress in nuclear magnetic resonance spectroscopy
A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems,...

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

Current opinion in chemical biology
Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the...