A Hybrid Approach to Increase the Informedness of CE-based Data Using Locus-Specific Thresholding and Machine Learning

• NCJ Number: 253320
• Author(s): Michael A. Marciano; Victoria R. Williamson; Jonathan D. Adelman
• Date Published: July 2018
• Length: 12 pages
• Grant Number(s): 2014-DN-BX-K029
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Award: A Hybrid Machine Learning Approach for DNA Mixture Interpretation

Annotation

This article describes a method that is able to collectively minimize the incorrect detection of non-allelic artifacts (false positives) and the threshold-induced dropout of true allelic information (false negatives) by accounting for baseline variability across instrument runs, samples, capillaries, dye-channels, injection times, and voltage that is unable to adapt, leading to a loss of allelic information that exists below the threshold.

Abstract

The described method is accomplished by using a dynamic locus and sample specific analytical threshold and a machine learning-derived probabilistic artifact detection model. The system produced an allele detection accuracy of 97.2 percent, an 11.4-percent increase from the lowest static threshold (50 RFU), with a low incidence of incorrectly identified artifacts (0.79 percent). This adaptive method outperformed static thresholds in the retention of allelic information content at minimal cost. (publisher abstract modified)