TY - JOUR
T1 - The generalisability of artificial neural networks used to classify electrophoretic data produced under different conditions
AU - Taylor, Duncan
AU - Kitselaar, Michael
AU - Powers, David
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Convolutional neural network
KW - Electropherogram
KW - Gel reading
UR - http://www.scopus.com/inward/record.url?scp=85056273377&partnerID=8YFLogxK
U2 - 10.1016/j.fsigen.2018.10.019
DO - 10.1016/j.fsigen.2018.10.019
M3 - Article
C2 - 30419517
AN - SCOPUS:85056273377
VL - 38
SP - 181
EP - 184
JO - Forensic Science International: Genetics
JF - Forensic Science International: Genetics
SN - 1872-4973
IS - 1
ER -