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Sara Tanilli

affiliation not provided to SSRN

  • affiliation not provided to SSRN

SCHOLARLY PAPERS

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Scholarly Papers (1)

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A rigorous UHPLC-HRMS untargeted workflow for Bipolar Disorder metabolic fingerprinting

Downloads 99 (699,467)

A rigorous UHPLC-HRMS untargeted workflow for Bipolar Disorder metabolic fingerprinting

Number of pages: 27 Posted: 05 Dec 2025
Sara Tanilli, Maria Mazzagatti, Rosangela Santalucia, Matteo Chiarello, Giovanni Solarino, Marco Pazzi, Alessandra Olarini, Marta massano, Paolo Garofano, Anna Bellicanta, Gabriele Lando, Marco Vincenti and Eugenio Alladio
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of Turin, University of Turin - Department of Chemistry, University of Turin, affiliation not provided to SSRN, University of Turin, Centro Regionale Antidoping "Alessandro Bertinaria", affiliation not provided to SSRN, affiliation not provided to SSRN, Universita degli Studi di Torino and University of Turin
Downloads 82 (798,777)

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Untargeted metabolomics, bipolar disorder, Compound Discoverer, high-dimensional data, machine learning

A rigorous UHPLC-HRMS untargeted workflow for Bipolar Disorder metabolic fingerprinting

Number of pages: 21 Posted: 10 Feb 2026
Sara Tanilli, Maria Mazzagatti, Rosangela Santalucia, Matteo Chiarello, Giovanni Solarino, Marco Pazzi, Alessandra Olarini, Marta massano, Paolo Garofano, Anna Bellicanta, Gabriele Lando, Marco Vincenti and Eugenio Alladio
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of Turin, University of Turin - Department of Chemistry, University of Turin, affiliation not provided to SSRN, University of Turin, Centro Regionale Antidoping "Alessandro Bertinaria", affiliation not provided to SSRN, affiliation not provided to SSRN, Universita degli Studi di Torino and University of Turin
Downloads 17 (1,548,841)

Abstract:

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Untargeted metabolomics, bipolar disorder, Compound Discoverer, high-dimensional data, machine learning