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Year | Number of Results |
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Page 1
Prediction of Type 2 Diabetes Based on Machine Learning Algorithm.
Int J Environ Res Public Health. 2021 Mar 23;18(6):3317. doi: 10.3390/ijerph18063317.
Int J Environ Res Public Health. 2021.
PMID: 33806973
Free PMC article.
Flexible Quality Control for Protein Turnover Rates Using d2ome.
Deberneh HM, Sadygov RG.
Deberneh HM, et al.
Int J Mol Sci. 2023 Oct 25;24(21):15553. doi: 10.3390/ijms242115553.
Int J Mol Sci. 2023.
PMID: 37958536
Free PMC article.
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Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling.
Deberneh HM, Sadygov RG.
Deberneh HM, et al.
J Proteome Res. 2023 Feb 3;22(2):410-419. doi: 10.1021/acs.jproteome.2c00592. Epub 2023 Jan 24.
J Proteome Res. 2023.
PMID: 36692003
Free PMC article.
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A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling.
Deberneh HM, Abdelrahman DR, Verma SK, Linares JJ, Murton AJ, Russell WK, Kuyumcu-Martinez MN, Miller BF, Sadygov RG.
Deberneh HM, et al.
Sci Data. 2023 Sep 19;10(1):635. doi: 10.1038/s41597-023-02537-w.
Sci Data. 2023.
PMID: 37726365
Free PMC article.
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Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.
Deberneh HM, Sadygov RG.
Deberneh HM, et al.
Int J Mol Sci. 2022 Nov 23;23(23):14620. doi: 10.3390/ijms232314620.
Int J Mol Sci. 2022.
PMID: 36498948
Free PMC article.
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Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling.
Deberneh HM, Abdelrahman DR, Verma SK, Linares JJ, Murton AJ, Russell WK, Kuyumcu-Martinez MN, Miller BF, Sadygov RG.
Deberneh HM, et al.
Commun Chem. 2023 Apr 17;6(1):72. doi: 10.1038/s42004-023-00873-x.
Commun Chem. 2023.
PMID: 37069333
Free PMC article.
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