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

TL;DR

deepNoC is a deep learning system that accurately estimates the number of contributors to a DNA profile using simulated data, with high performance and explainability, aiding forensic DNA analysis.

Contribution

This work introduces a novel pipeline for generating large labeled training data via simulation and applies deep neural networks to improve contributor estimation accuracy.

Findings

Achieves 89% accuracy in estimating 1-10 contributors.

Requires only a few hundred real profiles for fine-tuning.

Provides explainability features for user interpretation.

Abstract

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.