Setting the PACE

The two of us met at a whiteboard. We began talking about our two fields - forensic DNA interpretation and machine learning - and soon realized that this was a very promising match. The challenge of DNA mixture interpretation – specifically the number of contributors in a DNA mixture – is one that has existed since the field started; however, no one we are aware of has considered the use of machine learning to address that challenge − even though machine learning is ideally suited to handle classification problems with complex data sets. We further developed these ideas, found them to be unique and patentable, and submitted them to Syracuse University. The result is PACE, the Probabilistic Assessment for Contributor Estimation.

While there are several high quality probabilistic genotyping software suites available for DNA profiling, these tools often require knowledge of the number of contributors in order to strengthen their analyses. PACE provides that knowledge at very high speeds and low computational expenditures, as well as higher accuracy than previously used methods. With PACE, we can predict the number of contributors correctly in mixtures of up to four individuals with 99 percent accuracy. We suspect accurate prediction can be made for five or six individuals, and we’re in the process of developing and evaluating this.

Amping up Image Analysis