DNA Profiling: An Investigation of Six Machine Learning Algorithms for Estimating the Number of Contributors in DNA Mixtures

Abstract

DNA (Deoxyribonucleic acid) profiling involves analysis of sequences of individual or mixed DNA profiles to identify persons these profiles belong to. DNA profiling is used in important applications such as for paternity tests, in forensic science for person identification on a crime scheme, etc. Finding the number of contributors in a DNA mixture is a major task in DNA profiling with challenges caused due to allele dropout, stutter, blobs, and noise. The existing methods for finding the number of unknowns in a DNA mixture suffer from issues including computational complexity and accuracy of estimating the number of unknowns. Machine learning has received attention recently in this area but with limited success. Many more efforts are needed for improving the robustness and accuracy of these methods. Our research aims to advance the state-of-the-art in this area. Specifically, in this paper, we investigate the performance of six machine learning algorithms --Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Stochastic Gradient Descent (SGD), and Gaussian Naïve-Bayes (GNB) --applied to a publicly available dataset called PROVEDIt, containing mixtures with up to five contributors. We evaluate the algorithmic performance using confusion matrices and four performance metrics namely accuracy, F1-Score, Recall, and Precision. The results show that LR provides the highest Accuracy of 95% for mixtures with five contributors.

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“…Future work will also consider improving and refining the reference architecture, extending it through the development of services in other application domains and sectors including many smart city applications that we have developed over the years including smart cities [2,3,81], big data [8,20], improving computing algorithms [82,83], education [1], spam detection [84], accident and disaster management [85,86], autonomous vehicles and transportation [87][88][89][90][91], and healthcare [6,56,92,93].…”

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

“…Finally, the last work is our own work [6], where we investigated the performance of six machine learning algorithms for estimating the number of unknowns on a small subset (780 profiles) of the PROVEDIt dataset. The difference between this work and the work presented in this paper is that the previous work used machine learning algorithms as opposed to deep learning in this paper.…”

“…The challenges in this respect include estimating the number of contributors with high accuracy and within reasonable times. The methods that have been developed for the purpose can be divided into three categories: basic methods, high-performance computing (HPC) methods, and machine learning methods [6]. The basic methods rely on techniques such as likelihood ratio estimation and are usually very compute-intensive [7,8].…”

“…Machine learning methods, as is the case for many application domains, have been applied for estimating the number of unknowns. However, there is limited work in this area, with only four works in total [6]; many more efforts are required in terms of developing highly accurate models and their training on large and diverse datasets (see Section 3 for a detailed review of the relevant works).…”

“…Earlier, we developed the first distributed-memory HPC implementations [11] of DNA profiling using maximum likelihood ratio computations and reported results for up to ten unknowns delivering over 15× the performance using over 3000 cores. In another recent work [6], we investigated the performance of six machine learning algorithms for estimating the number of unknowns on a small subset (780 profiles) of the PROVEDIt dataset without exploring the performance across various multiplexes, loci, and profiles.…”