The generalisability of artificial neural networks used to classify electrophoretic data produced under different conditions

Duncan Taylor, Michael Kitselaar, David Powers

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Previous work has shown that artificial neural networks can be used to classify signal in an electropherogram into categories that have interpretational meaning (such as allele, baseline, pull-up or stutter). The previous work trained the neural networks on a single data type, produced under a single laboratory condition and applied it to data that was matched in these factors. In this work we investigate the ability of neural networks to be trained on data of different types (i.e. single sourced profiles or mixed DNA profiles) and from different laboratory conditions (specifically the model of electrophoresis instrument) to determine whether a set of neural networks is required for each different type of data produced or whether a single neural network can be used for a broad range of data and still achieve the same level of performance. The results of our study have implications as to how a laboratory would choose to train and apply neural networks to classify data in electropherograms produced in their laboratory.

Original languageEnglish
Pages (from-to)181-184
Number of pages4
JournalForensic Science International: Genetics
Volume38
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Artificial neural network
  • Convolutional neural network
  • Electropherogram
  • Gel reading

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