Ahmad Mani‐Varnosfaderani
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Also affiliated: University of Bern (2012); Sharif University of Technology (2009–2014); Consejo Superior de Investigaciones Científicas (2020); University of British Columbia (2022–2023); Tarbiat Modares University (2014–2026); University of Arkansas Medical Center (2026); Institute of Environmental Assessment and Water Research (2020); Arkansas Children's Nutrition Center (2025–2026); Institute for Research in Fundamental Sciences (2018)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Ahmad Mani‐Varnosfaderani's research focuses on the application of machine learning and chemometric tools for analytical chemistry and metabolomics. He has investigated the use of nanoparticles and magnetic materials for sample preparation and detection of various analytes, including heavy metal ions and antibiotics. His work also includes the development of gas sensors for amine detection and chromatographic fingerprint analysis of natural products. A significant portion of his recent publications explores metabolomic profiling of seminal plasma in men with infertility, aiming to identify noninvasive biomarkers for spermatogenesis and infertility.
Metrics
- h-index: 18
- Publications: 87
- Citations: 1,017
Selected Publications
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From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation (2026)
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Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra (2026)
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MS2FinProp_Data_Files (2026)
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MS2FinProp_Data_Files (2026)
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Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery (2025)
Collaboration Network
Top Collaborators
- Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery
- Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery
- Beyond the Known Metabolome: Predictive Strategies for Dark Matter Discovery
- Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra
- Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra
- Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra
- Integrating fingerprint prediction and physicochemical property filtering to annotate unknown metabolites from high-resolution MS/MS spectra
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation
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