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Gaussian Process Modeling of Protein Turnover
Mahbubur Rahman
, Stephen F. Previs
, Takhar Kasumov
,
Rovshan G. Sadygov
Biochemistry & Molecular Biology
Research output
:
Contribution to journal
›
Article
›
peer-review
24
Scopus citations
Overview
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Dive into the research topics of 'Gaussian Process Modeling of Protein Turnover'. Together they form a unique fingerprint.
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Keyphrases
Protein Turnover
100%
Stochastic Model
100%
Gaussian Process Model
100%
Mouse Brain
66%
Stochastic Modeling
66%
Curve Fitting
66%
Liver Proteins
66%
Non-stochastic
66%
Liver
33%
Stable Isotope Labeling
33%
Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS)
33%
Mouse Liver
33%
4-fold
33%
High-throughput
33%
Brain Proteins
33%
Heart Failure
33%
Protein Production
33%
Protein Decay
33%
Heavy Water
33%
Metabolic Labeling
33%
QR Code
33%
Freely Available
33%
15N Labeling
33%
Data-centric
33%
Degradation Rate Constant
33%
Performance Metrics
33%
Covariance Matrix
33%
Large-scale Dataset
33%
Gaussian Process
33%
One-compartment
33%
Explicit Solution
33%
Residual Sum of Squares
33%
Ornstein-Uhlenbeck
33%
In Vivo Protein Turnover
33%
Heavy Water Metabolic Labeling
33%
Decay Rate Constant
33%
Turnover Process
33%
Stochastic Processes
33%
Stochastic Differential Equations
33%
Neuroscience
Liver Protein
100%
In Vivo
50%
Liquid Chromatography-Mass Spectrometry
50%
Brain Protein
50%
Stochastic Process
50%
Mouse Brain
50%
Biochemistry, Genetics and Molecular Biology
Protein Degradation
100%
Gaussian Distribution
100%
Isotope Labeling
50%
Solution and Solubility
25%
Liquid Chromatography-Mass Spectrometry
25%
Food Science
Protein Degradation
100%
Liquid Chromatography Mass Spectrometry
25%