1. The generalisability of artificial neural networks used to classify electrophoretic data produced under different conditions.
- Author
-
Taylor, Duncan, Kitselaar, Michael, and Powers, David
- Subjects
SHORT tandem repeat analysis ,ARTIFICIAL neural networks ,DNA fingerprinting ,ELECTROPHOTOGRAPHY ,ALLELES ,DNA analysis ,ELECTROPHORESIS - Abstract
Highlights • We apply Artificial Neural Networks to electrophotographic data to identify features of STR DNA profiles. • DNA profiles can be produced under different conditions with respect to laboratory hardware, protocols used and template source. • We trialled the ability of neural networks to generalise across the different factors involved in generation of profiles. • A single neural network was able to be trained on, and applied to, data produced under all different conditions. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF