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Filippo Barni

Reparto CC Investigazioni Scientifiche di Roma

SCHOLARLY PAPERS

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Scholarly Papers (2)

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1.

Analysis of Rapidly Mutating Y-Strs Enables Almost Complete Discrimination of Unrelated and Related Males from the African Continent

Number of pages: 19 Posted: 02 May 2024
Filippo Barni, Arwin Ralf, Chiara Della Rocca, Federica Cannistrà, Marco Gigliucci, Beniamino Trombetta, Andrea Berti, Manfred Kayser and Fulvio Cruciani
Reparto CC Investigazioni Scientifiche di Roma, affiliation not provided to SSRN, Carabinieri RIS, Sapienza University of Rome, affiliation not provided to SSRN, Sapienza University of Rome, Reparto CC Investigazioni Scientifiche di Cagliari, Erasmus University Rotterdam (EUR), Medical Center, Department of Genetic Identification and affiliation not provided to SSRN
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RM Y-STR, RMplex, Haplotype, Yfiler Plus, Africa, Y chromosome

2.

An Initial Exploration of Machine Learning for Establishing Associations between Genetic Markers and THC Levels in Cannabis Sativa Samples

Number of pages: 26 Posted: 02 Feb 2024
Selena Cisana, Michele Di Nunzio, Valentina Brenzini, Monica Omedei, Fabrizio Seganti, Christina Ververi, Enrico Gerace, Alberto Salomone, Andrea Berti, Filippo Barni, Sergio Schiavone, Andrea Coppi, Ciro Di Nunzio, Paolo Garofano and Eugenio Alladio
Centro Regionale Antidoping "Alessandro Bertinaria", University of Barcelona - Forensic Genetics Laboratory - Legal Medicine Unit, University of Florence, Centro Regionale Antidoping "Alessandro Bertinaria", Centro Regionale Antidoping "Alessandro Bertinaria", Centro Regionale Antidoping "Alessandro Bertinaria", Centro Regionale Antidoping "Alessandro Bertinaria", University of Turin, Reparto CC Investigazioni Scientifiche di Cagliari, Reparto CC Investigazioni Scientifiche di Roma, Reparto CC Investigazioni Scientifiche di Roma, University of Barcelona, University of Naples Federico II, Centro Regionale Antidoping "Alessandro Bertinaria" and University of Turin
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Cannabis Sativa, DNA typing, Δ9-THC content, Multivariate Statistical Analysis, Machine Learning