The Bioforensics group research focus is at the intersection of genetic identity, DNA based forensic science and issues pertaining to national security. We leverage molecular biology and bioinformatics to address applied scientific questions relating to (1) DNA based geolocation – the exploitation of biologicals to aid in tagging, tracking locating targets of interest and (2) laboratory and computational approaches to DNA mixture interpretation.
- Taylor Cahalan
- Kacey Christian
- Molly Dunegan
- D. Spencer Eberst
- Michael Maloney
- Ebrar Mohammad
- Angie Zhao
In the News
- Syracuse University researchers develop new method of identifying evidence in sexual assault cases – Feb 2018.
- Biotechnology research isn’t slowing down anytime soon at Syracuse University – Feb 2018.
- ISHI news: How Did You Become Interested in Forensics? – Feb 2018.
- ISHI news: What is the Biggest Challenge Forensics Laboratories Face Today? – Feb 2018.
- ISHI news: Under the Microscope – Jonathan Adelman & Michael Marciano – Sep 2017
- DNA Testing Data Is Disturbingly Vulnerable to Hackers – Gizmodo – Aug 2017
- New Forensics Approaches Looking More “CSI”-Like – Apr 2017
- How Machine Learning Is Changing Crime-Solving Tactics – Feb 2017
- Residue on cell phones used to create ‘portraits’ of users – Nov 2016.
- Forensics organization to use grant for DNA profiling in rape cases – Oct 2015.
- Silicon Biosystems, Syracuse University Form Forensic Tech Alliance – Oct 2015.
- Syracuse to Acquire Cutting-Edge DNA Sequencer – Aug 2015.
- Once Upon a Crime – Feb 2015.
- FNSSI Scientists Awarded National Institute of Justice Award – Oct 2014.
- Seven cool research projects Syracuse University profs are pursuing – Oct 2014.
- Genome sequencing highlights risks of diseases – Feb 2014.
- Marciano, M. A. & Adelman, J. D. (2017). PACE: Probabilistic Assessment for Contributor Estimation—A machine learning-based assessment of the number of contributors in DNA mixtures. Forensic Science International: Genetics, 27, 82-91. DOI: http://dx.doi.org/10.1016/j.fsigen.2016.11.006
- Marciano M.A.,Panicker S.X., Liddil G.D., Lindgren D. & Sweder K.S. Development of a Method to Extract Opium Poppy (Papaver somniferum L.) DNA from Heroin. Nature Scientific Reports 8 (2018) 2590. doi:10.1038/s41598-018-20996-9
- Williamson, V.R, Laris T.M., Romano R. Marciano & M.A. Enhanced DNA Mixture Deconvolution of Sexual Offense Samples Using the DEPArray™ System. Forensic Science International: Genetics. 34(2018) 265-276). DOI: https://doi.org/10.1016/j.fsigen.2018.03.001
- Marciano M.A., Williamson V.R. & Adelman J.D. A Hybrid Approach to Increase the Informedness of CE-based Data Using Locus-Specific Thresholding and Machine Learning. Forensic Science International: Genetics. https://doi.org/10.1016/j.fsigen.2018.03.017.
A Hybrid Machine Learning Approach for DNA Mixture Interpretation
The Bioforensics group has developed a DNA mixture deconvolution software to explore the use of machine learning as a potential method for enhanced deconvolution. The software related to this initial proof of concept study can be downloaded here. Please contact us for any questions or support needs.
This project was supported by Award No. 2014-DN-BX-K029 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect those of the Department of Justice.
Patent Pending: SYSTEM AND METHOD FOR INTER-SPECIES DNA MIXTURE INTERPRETATION 20160162636
Inventors: Michael A. Marciano and Jonathan D Adelman
Abstract: Methods and systems for characterizing two or more nucleic acids in a sample. The method can include the steps of providing a hybrid machine learning approach that enables rapid and automated deconvolution of DNA mixtures of multiple contributors. The input is analyzed by an expert system which is implemented in the form of a rule set. The rule set establishes requirements based on expectations on the biology and methods used. The methods and systems also include a machine learning algorithm that is either incorporated into the expert system, or utilizes the output of the expert system for analysis. The machine learning algorithm can be any of a variety of different algorithms or combinations of algorithms used to perform classification in a complex data environment.