Carsten Prasse
Biographic Data
| ID | 6928452 |
|---|---|
| NAME | Carsten Prasse |
| GIVEN NAMES | Carsten |
| FAMILY NAME | Prasse |
| SIGNATURE | PRASSE C |
| AFFILIATIONS | Johns Hopkins University |
| ORCID | 0000-0002-1470-141X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
AI redefine untargeted metabolomics
The Human Exposome Project aims to map the totality of environmental exposures, but its success relies on transforming qualitative detections into quantitative data. Following our review on AI-driven metabolite identification, this second installment addresses the next critical bottleneck: estimating chemical concentrations in untargeted metabolomics without authentic standards. Translating LC-HRMS signal intensities into absolute concentrations …
AI redefines mass spectrometry chemicals identification
The comprehensive identification of environmental and endogenous chemicals in human biospecimens is a critical bottleneck for realizing the Human Exposome Project. Untargeted metabolomics, particularly liquid chromatography–high-resolution mass spectrometry (LC–HRMS), offers unparalleled coverage of small molecules, but most detected features remain unidentified due to limited spectral libraries and structural ambiguity. Retention time (RT) predi…
Reimagining safe drinking water on the basis of twenty-first-century science
No prominent works on this page.
Reimagining safe drinking water on the basis of twenty-first-century science
AI redefines mass spectrometry chemicals identification
The comprehensive identification of environmental and endogenous chemicals in human biospecimens is a critical bottleneck for realizing the Human Exposome Project. Untargeted metabolomics, particularly liquid chromatography–high-resolution mass spectrometry (LC–HRMS), offers unparalleled coverage of small molecules, but most detected features remain unidentified due to limited spectral libraries and structural ambiguity. Retention time (RT) predi…
AI redefine untargeted metabolomics
The Human Exposome Project aims to map the totality of environmental exposures, but its success relies on transforming qualitative detections into quantitative data. Following our review on AI-driven metabolite identification, this second installment addresses the next critical bottleneck: estimating chemical concentrations in untargeted metabolomics without authentic standards. Translating LC-HRMS signal intensities into absolute concentrations …
Computational Drug Discovery Methods (2 works) · Exposome (2 works) · Health, Environment, Cognitive Aging (2 works) · Human health (2 works) · Metabolomics (2 works) · Metabolomics and Mass Spectrometry Studies (2 works) · Annotation (1 work) · Artificial Intelligence (1 work) · Biology (1 work) · Business (1 work)