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

NCJ Number
253320
Date Published
July 2018
Length
12 pages
Author(s)
Michael A. Marciano; Victoria R. Williamson; Jonathan D. Adelman
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored), Program/Project Description
Grant Number(s)
2014-DN-BX-K029
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)
Date Created: July 20, 2021