AIMC Topic: Metabolomics

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Systems biology in cardiovascular disease: a multiomics approach.

Nature reviews. Cardiology Dec 18, 2020
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for anal...
Machine Learning Cardiovascular Diseases Gene Regulatory Networks Humans Neural Networks, Computer Proteomics Metabolomics Lipidomics Computational Biology Systems Biology Genomics
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A Customizable Analysis Flow in Integrative Multi-Omics.

Biomolecules Nov 27, 2020
The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology...
Metabolomics Systems Biology Humans Proteomics Machine Learning Mass Spectrometry
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A random forest based biomarker discovery and power analysis framework for diagnostics research.

BMC medical genomics Nov 23, 2020
BACKGROUND: Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of bi...
Decision Trees Sample Size Lipidomics Datasets as Topic Workflow Molecular Diagnostic Techniques Metabolomics Regression Analysis Computer Simulation Humans Transcriptome Algorithms Biomarkers Machine Learning
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Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum.

Molecular cancer research : MCR Nov 9, 2020
Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to...
Male Metabolomics Prostatic Neoplasms Humans Machine Learning Neoplasm Grading
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Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine.

Journal of clinical laboratory analysis Nov 3, 2020
OBJECTIVE: Development of biofluid-based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking. Thus, here we aimed to identify serum metabolic biomarkers for the diagnosis of COPD.
Artificial Intelligence Biomarkers Female Humans Diagnosis, Computer-Assisted Male Support Vector Machine Least-Squares Analysis Middle Aged Aged Magnetic Resonance Spectroscopy Case-Control Studies Metabolomics Pulmonary Disease, Chronic Obstructive
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Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.

Personalized medicine Oct 30, 2020
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized ...
Remote Sensing Technology Humans Depressive Disorder, Major Machine Learning Schizophrenia Mental Disorders Bipolar Disorder Big Data Early Diagnosis Metabolomics
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MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

Metabolomics : Official journal of the Metabolomic Society Oct 21, 2020
INTRODUCTION: Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result,...
Mass Spectrometry Machine Learning Metabolomics Software Chromatography, Liquid
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Predicting human health from biofluid-based metabolomics using machine learning.

Scientific reports Oct 19, 2020
Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant feat...
Metabolomics Databases, Factual Models, Theoretical Health Status Humans Machine Learning
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Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks.

Journal of bioscience and bioengineering Oct 10, 2020
Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation o...
Software Metabolomics Chromatography Deep Learning
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Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics.

Analytical chemistry Aug 12, 2020
Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. ...
Deep Learning Humans Metabolomics Gas Chromatography-Mass Spectrometry Machine Learning
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