Mass spectrometry‐based proteomic platforms for better understanding of SARS‐CoV‐2 induced pathogenesis and potential diagnostic approaches is a research paper published in PROTEOMICS (2021). On theSindex it has a DataRank of 0. It has been cited 36 times.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Institutes of Health
Grant: R01GM133840
National Institutes of Health
Grant: R01HL133624
National Institutes of Health
Grant: R01GM116116
National Science Foundation
Grant: OCE‐1634630
National Institute of General Medical Sciences
Grant: R01GM123055
National Institutes of Health
Grant: 1R01GM133840-01A1
Building protein structure models for intermediate resolution cryo-electron microscopy maps
National Institutes of Health
Grant: 5R01GM116116-04
From Single Cells to Tissues: a Novel Mass Spectrometry Approach for Bioanalysis
National Institutes of Health
Grant: 1R01HL133624-01A1
Sub-cellular Targeting of Endothelial ROS in Myocardial Ischemia
National Institutes of Health
Grant: 5R01GM123055-02
Structural Modeling of Multifarious Protein Complexes
NHLBI NIH HHS
Grant: R25 HL088992
NSF
Grant: OCE-1634630
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 2 of Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls
Additional file 2 of Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls
Additional file 2 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 2 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 4 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 4 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 5 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 5 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 6 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 6 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 7 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 7 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 3 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 1 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 3 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 1 of Dysregulated proteasome activity and steroid hormone biosynthesis are associated with mortality among patients with acute COVID-19
Additional file 1 of Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls
Additional file 1 of Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls