deepNoC: A deep learning system to assign the number of contributors to a short tandem repeat DNA profile

A common task in forensic biology is to interpret and evaluate short tandem repeat DNA profiles. The first step in these interpretations is to assign a number of contributors to the profiles, a task that is most often performed manually by a scientist using their knowledge of DNA profile behaviour. Studies using constructed DNA profiles have shown that as DNA profiles become more complex, and the number of DNA-donating individuals increases, the ability for scientists to assign the target number. There have been a number of machine learning algorithms developed that seek to assign the number of contributors to a DNA profile, however due to practical limitations in being able to generate DNA profiles in a laboratory, the algorithms have been based on summaries of the available information. In this work we develop an analysis pipeline that simulates the electrophoretic signal of an STR profile, allowing virtually unlimited, pre-labelled training material to be generated. We show that by simulating 100 000 profiles and training a number of contributors estimation tool using a deep neural network architecture (in an algorithm named deepNoC) that a high level of performance is achieved (89% for 1 to 10 contributors). The trained network can then have fine-tuning training performed with only a few hundred profiles in order to achieve the same accuracy within a specific laboratory. We also build into deepNoC secondary outputs that provide a level of explainability to a user of algorithm, and show how they can be displayed in an intuitive manner.

A novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network

The application of Shapley values to high-dimensional, time-series-like data is computationally challenging - and sometimes impossible. For $N$ inputs the problem is $2^N$ hard. In image processing, clusters of pixels, referred to as superpixels, are used to streamline computations. This research presents an efficient solution for time-seres-like data that adapts the idea of superpixels for Shapley value computation. Motivated by a forensic DNA classification example, the method is applied to multivariate time-series-like data whose features have been classified by a convolutional neural network (CNN). In DNA processing, it is important to identify alleles from the background noise created by DNA extraction and processing. A single DNA profile has $31,200$ scan points to classify, and the classification decisions must be defensible in a court of law. This means that classification is routinely performed by human readers - a monumental and time consuming process. The application of a CNN with fast computation of meaningful Shapley values provides a potential alternative to the classification. This research demonstrates the realistic, accurate and fast computation of Shapley values for this massive task

Simulating realistic short tandem repeat capillary electrophoretic signal using a generative adversarial network

DNA profiles are made up from multiple series of electrophoretic signal measuring fluorescence over time. Typically, human DNA analysts 'read' DNA profiles using their experience to distinguish instrument noise, artefactual signal, and signal corresponding to DNA fragments of interest. Recent work has developed an artificial neural network, ANN, to carry out the task of classifying fluorescence types into categories in DNA profile electrophoretic signal. But the creation of the necessarily large amount of labelled training data for the ANN is time consuming and expensive, and a limiting factor in the ability to robustly train the ANN. If realistic, prelabelled, training data could be simulated then this would remove the barrier to training an ANN with high efficacy. Here we develop a generative adversarial network, GAN, modified from the pix2pix GAN to achieve this task. With 1078 DNA profiles we train the GAN and achieve the ability to simulate DNA profile information, and then use the generator from the GAN as a 'realism filter' that applies the noise and artefact elements exhibited in typical electrophoretic signal.