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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
Journal
Forensic Science International-Genetics Volume: 35 Dated: July 2018 Pages: 26-37
Date Published
July 2018
Length
12 pages
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)

Grant Number(s)
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical)
Report (Study/Research)
Report (Grant Sponsored)
Program/Project Description

Date Published: July 1, 2018