Author: Partho Protim Ghosh
Ghosh, Partho Protim, 2020 How well a neural network system could interpret the number of contributors to a DNA Profile, Flinders University, College of Science and Engineering
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Machine learning approaches and artificial neural network (ANN) are very popular approaches to improve the efficiency in many fields including economics, biology, finance, image processing and gaming. It reduces the manual effort where complex interpretation and analysis are required. The artificial neural network (ANN) and machine learning (ML) are quite new approach in the field of DNA analysis and there are lots of prospects which can be visualised by this technology. There are several goals included in the research work: Create a system using the extensions of ANN such as Recurrent Neural Networks (RNN), or Convolutional Neural Networks (CNN) that could interpret the number of contributors to a DNA profile. The research can be divided into four components: Simulate realistic DNA profile with a probability in accordance to their population frequency database (provided by FSSA named Australia_Caucasian.csv); Simulate DNA profile for single contributor and mix the single profiles to generate mixture of DNA profile for different contributors (Number of contributors in mixture can be 2/3/4/5); Divide the profiles in 21 smaller parts (Each part represents a Locus and DNA profile consists with 21 locus); Prepare the mix profiles and single contributor profiles to train the Neural Networks; Train the Convolutional Neural Networks (CNNs) with the input profiles (Mix Profiles) and output profiles (Individual Profiles) for all 21 Loci and evaluate the performance.
Keywords: DNA, DNA profiling, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Forensics, Bioinformatics
Subject: Bioinformatics thesis
Thesis type: Masters
Completed: 2020
School: College of Science and Engineering
Supervisor: Professor David M W Powers