TY  - JOUR
AU  - Bouakaze, Caroline
AU  - Delehelle, Franklin
AU  - Saenz-Oyhéréguy, Nancy
AU  - Moreira, Andreia
AU  - Schiavinato, Stéphanie
AU  - Croze, Myriam
AU  - Delon, Solène
AU  - Fortes-Lima, César
AU  - Gibert, Morgane
AU  - Bujan, Louis
AU  - Huyghe, Eric
AU  - Bellis, Gil
AU  - Calderón Fernández, María Del Rosario
AU  - Hernández, Candela 
AU  - Avendaño-Tamayo, Efren
AU  - Bedoya, Gabriel
AU  - Salas, Antonio
AU  - Mazières, Stéphane
AU  - Chiaroni, Jacques
AU  - Migot-Nabias, Florence
AU  - Ruiz-Linares, Andres
AU  - Dugoujon, Jean-Michel
AU  - Théves, Catherine
AU  - Mollereau-Manaute, Catherine
AU  - Nôus, Camille
AU  - Poulet, Nicolas
AU  - King, Turi
AU  - D'Amato, Maria Eugenia
AU  - Balaresque, Patricia
PY  - 2020
DO  - 10.1016/j.fsigen.2020.102342
SN  - 1872-4973
UR  - https://hdl.handle.net/20.500.14352/98074
T2  - Forensic Science International: Genetics
AB  - We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high...
LA  - eng
M2  - 102342
PB  - Science Direct
KW  - Y-STR
KW  - Machine learning
KW  - Assignation accuracy and haplogroup prediction (Hg prediction)
KW  - Incremental mutation rates
TI  - Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters
TY  - journal article
VL  - 48
ER  -