Papers describing the validation, features and models of FaSTR™ DNA:

[1] D. Taylor, A. Harrison, D. Powers, An artificial neural network system to identify alleles in reference electropherograms. Forensic science international. 2017; Genetics 30 114-126.

[2] R.M. Goor, L. Forman Neall, D. Hoffman, S.T. Sherry, A mathematical approach to the analysis of multiplex DNA profiles. Bulletin of mathematical biology.  2011; 73(8) 1909-1931.

[3] M.-H. Lin, S.-I. Lee, X. Zhang, L. Russell, H. Kelly, K. Cheng, S. Cooper, R. Wivell, Z. Kerr, J. Morawitz, J.-A. Bright, Developmental validation of FaSTR™ DNA: Software for the analysis of forensic DNA profiles. Forensic Science International: Reports. 2021; Volume 3, 100217. 

[4] D. Taylor, D. Powers, Teaching artificial intelligence to read electropherograms, Forensic Science International: Genetics 25 (2016) 10-18.

[5] D. Taylor, M. Kitselaar, D. Powers, The generalisability of artificial neural networks used to classify electrophoretic data produced under different conditions, Forensic Science International: Genetics 38 (2019) 181-184.

[6] M. Kruijver, H. Kelly, K. Cheng, M.H. Lin, J. Morawitz, L. Russell, J. Buckleton, J.A. Bright, Estimating the number of contributors to a DNA profile using decision trees, Forensic science international. Genetics 50 (2021) 102407.

[7] L. Volgin, D. Taylor, J.-A. Bright, M.-H. Lin, Validation of a neural network approach for STR typing to replace human reading, Forensic Science International: Genetics (2021) 102591.

[8] T. Kalafut, C. Schuerman, J. Sutton, T. Faris, L. Armogida, J.-A. Bright, J. Buckleton, D. Taylor, Implementation and validation of an improved allele specific stutter filtering method for electropherogram interpretation, Forensic Science International: Genetics 35 (2018) 50-56.



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