Validation of a neural network approach for STR typing to replace human reading

Luke Volgin, Duncan Taylor, Jo Anne Bright, Meng-Han Lin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A typical forensic laboratory process for interpreting STR capillary electrophoresis profile data is for two people to independently ‘read’ the profiles, compare results, and resolve any differences. Recently, work has been conducted to develop a machine learning tool called an artificial neural network (ANN) to carry out the same function as a human profile reader, by classifying areas of fluorescence in the capillary electrophoresis profile raw signal data. The ANN approach has been embedded in commercial software FaSTR™ DNA to read GlobalFiler™ DNA profiles. The ANN feature of FaSTR™ DNA was investigated during validation at Forensic Science South Australia (FSSA) to determine whether one of the human profile readers could be replaced by an ANN reader. FaSTR™ DNA accuracy in detecting allele peaks in reference profiles was 99.7% and was deemed high enough that a one-reader workflow could be implemented into the reference reading workflow at FSSA.

Original languageEnglish
Article number102591
Number of pages8
JournalForensic Science International: Genetics
Volume55
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Artificial intelligence
  • Gel reading
  • GeneMapper
  • Neural network
  • Reference profile

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