DNA profiling software suite that determines the number of contributors in a DNA sample with 98% accuracy.
DNA mixtures with more than two contributors can be difficult for laboratories to interpret. It can be even more challenging with traditional software to determine how many people contributed to one DNA sample.
Through the patented Probabilistic Assessment for Contributor Estimate (PACE™), developed by Syracuse University, NicheVision aims to solve these problems.
PACE™ serves as a plug-in for DNA profiling software suites. Using machine learning, a form of artificial intelligence, PACE™ provides the number of up to four contributors in a DNA sample with 98% accuracy. PACE™ can classify mixtures in less than one second using a standard laptop or desktop computer – compared to the typical nine hours it takes through current methods.
PACE™ is improving DNA mixture interpretation and setting trends for the entire DNA analysis industry. This machine learning-based method outperformed a human expert by 6% on a three-person sample and 20% on a four-person sample. (Research findings featured in Forensic Science International: Genetics). Overall, researchers believe PACE™ is representative of the future in how DNA is analyzed.
By incorporating innovative technology into DNA analysis software, PACE™ is providing quick, accurate results from difficult-to-read samples, ultimately saving laboratories time and money.
PACE™ performs the following functions:
- Constructs classification models using feature vectors containing any type of numeric, nominal, binary or character-based data, which can then be integrated into DNA sequence-based forensic workflows
- Learns predictive models through a machine-learning algorithm based off size, quality and characteristics of associated training data
- Imports data from genetics and DNA analysis software
- Preprocesses exported fragment data, including the application of stutter filters and an analytical threshold
- Scales and selects data categories, such as peak height and allele count per locus
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