An artificial neural network system to identify alleles in reference electropherograms

Abstract

Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system.

Towards adaptive method for peak migration time correction: discretization period in electropherograms

“…This can be achieved via (a) programmed algorithm, that needs deeper understanding between a separation current and a discretization period model, or (b) machine learning/artificial intelligence methods capable of handling time-series. In the main artificial intelligence method group -neural networks, the developed model is based on linear sections that form a separating surface in a multidimensional space [32]. In this work, the compensation method is also based on linear segments that warp the time scale according to the references.…”

Artificial Neural Networks in Forensic Medicine (review)

“…Thus, the researches of electropherograms are held in a huge quantityin forensic DNA laboratories.Genetic experts should be involved in the correct interpretation of the obtained data. [19] In the work [20] , we can see data concerning the using ofANN for the identification and classification of peaks in the electropherogram. The authors note that the process of interpretation of the electropherogram can be time consuming and depends on the subjective evaluation of the analyst.…”

Design and applications of a neural networks assisted portable liquid surface tensiometer

“…3 -polynomial and logarithmic fitting of the retraction force and concentrations. On the other hand, NN models are used for numerous different fields, including life sciences, medicine, and analytical chemistry [22][23][24].…”