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) the interpretation of low quality, low quantity DNA samples, (2) laboratory and computational (machine learning) approaches to facets of DNA mixture interpretation and (3) DNA based geolocation – the exploitation of biologicals to aid in tagging, tracking locating targets of interest.
Faculty Team Members
- PI: Michael A Marciano Ph.D., Research Associate Professor, Director of Research-Forensic & National Security Sciences Institute
College of Arts and Sciences : Forensic & National Security Sciences Institute, More About Michael Marciano
- Morgan Frank
- Zachary Herzog
- Cameron Moore
- Jenny Nguyen
- Natalia Pedraza
- Veronica Poole
- Amani Rafiq
- Julia Sousa
- Megan Voshage
- Taylor Walther
- Recent student achievements
- Morgan Frank – Internship at the Hennepin County Sheriff’s Office Forensic Science Laboratory (MN) (2023)
- Cameron Moore – Internships at the Michigan State Police Forensic Laboratory (2023)
- Veronica Poole – SOURCE grant 2023
- Haley Crooks – Internship at the San Diego Police Department Crime Lab (2022)
- Natalia Pedraza – SOURCE grant 2022-2023
- Hiring news – recent Bioforensics research alumni!
- Taylor Zekri – Forensic Scientist (DNA) – Minnesota Bureau of Criminal Apprehension
- Haley Crooks – Forensic Scientist (DNA) – City of San Diego Police Department Crime Laboratory (Conditional Offer)
- Amber Vandepoele – Forensic Scientist (DNA) – Oregon State Police
- Jonathan Hogg – Forensic Biologist (DNA) at the New York State Police Forensic Investigation Center
- Bridget Essing – Scientist in the Department of Defense
- Recent student achievements
- Bioforensics data now publicly available through a collaboration with the Rutgers – POWERPLEX FUSION CELL SORTED HALF-REACTION DATA ADDED 06/20/2020.
- Please find more information regarding our probabilistic method to predict the number of contributors here: https://nichevision.com/pace/
In the News
- Reunited (Reader’s Digest) – August 2023
- Hawaii wildfires: The painstaking work to identify the dead – August 2023
- Forensics Professor Quoted in Newsweek and Fox News – December 2022
- Idaho Police Warn Community to Remain Vigilant (Interview begins at 10:52 a.m.) – November 2022
- What DNA Found at Idaho Murder Scene Could Reveal About Killer – November 2022
- How Idaho Police Ruled Out Roommates as Suspects in Gruesome Murders – November 2022
- State’s Attorney Mosby says DNA test results will determine whether she drops Adnan Syed’s charges – September 2022
- Forensics Professor Explores New Technology to Improve DNA Detection – August 2022
- ‘Seeing Possibility For Myself’: SUSTAIN Program Continues to Cultivate, Support STEM Talent – April 2022
- Forensic Science: What Was Learned From 9/11 – September 2021
- Syracuse University Students Learn How Forensic Science Is Still Identifying 9/11 World Trade Center Victims 20 Years After The Attack – September 2021
- 32nd International Symposium on Human Identification (2021)
- Amber Vandepoele named an International Symposium on Human Identification (ISHI) Student Ambassador 2021
- Previewing the Posters of Our ISHI Student Ambassadors: Amber Vandepoele
- Under the Microscope with ISHI Student Ambassador Amber Vandepoele
- Bridget Essing ’22 Presents her Thesis Research at the 2021 ISHI Conference
- A&S Forensic Scientists Design the First Machine Learning Approach to Forensic DNA Analysis – July 2021
- FNSSI’s Mike Marciano Appointed to Committee that Oversees NYS Forensic Lab Accreditation – April 2021
- Company news: Michael Marciano appointed to NYS Commission on Forensic Sciences – May 2021
- Forensic Technology Center of Excellence Webinar: An Automated Single Cell Separation Technique to Improve Mixture Deconvolution – November 2020
- Following the Digital Trail – October 2020
- Forensic Technology Center of Excellence Webinar: PACE™: Rapid and Automated Artifact Identification and Number of Contributor Prediction – July 2020
- DNA: The Building Blocks of Achievement – October 2019
- Setting the PACE: Forensics and National Security Sciences Institute Develops DNA Tool – October 2019
- NIJ and Syracuse University Success Story: Improving DNA Mixture Interpretation with the Help of Machine Learning – April 2019
- The New Weapon in the Fight Against Crime – March 2019
- Unraveling the Genetic Mysteries of the Opium Poppy – Aug 2018
- How a DNA test 30 years later silenced murder convict Timothy Vail’s bid for freedom – Aug 2018
- 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. and Maynard H. (2023). Enhancing Research and Collaboration in Forensic Science: A Primer on Data Sharing Forensic Science International: Synergy. 100323, ISSN 2589-871X, https://doi.org/10.1016/j.fsisyn.2023.100323.
- D’Angelo O., Vandepoele A.C.W., Adelman, J. and Marciano, MA. (2022). Assessing non-LUS stutter in DNA sequence data. Forensic Science International: Genetics. 59,102706. https://doi.org/10.1016/j.fsigen.2022.102706.
- Marciano, M.A. and Crill, J. (2021). Genetics and Biodiversity: Tools to Improve Geolocation and Source Attribution. Research Shorts. National Intelligence Press Publications. November 8, 2021. https://ni-u.edu/wp/wp-content/uploads/2022/01/NIUShort_11082021_DNI202104183.pdf
- Watkins, D., Myers, D., Xavier, H., and Marciano, M. (2021). Revisiting Single Cell Analysis in Forensic Science. Nature Scientific Reports 11, 7054.10.1038/s41598-021-86271-6. [OPEN ACCESS]
- Young, B., Marciano, M., Crenshaw, K., Duncan, G., Armogida, L., and McCord, B. (2021) Match Statistics for Sequence-Based Profiles from Forensic PCR-MPS Kits. Electrophoresis 0, 1-10. https://doi.org/10.1002/elps.202000087.
- Malanio BP, Mehta, P, Kurimsky MT, Marciano, MA. (2020) Quantification of Cellular Material on Fired and Unfired Ammunition. AFTE Journal 52(4), 230-238.
- Marciano, Michael & Adelman, Jonathan. (2019). Developmental Validation of PACE™: Automated Artifact Identification and Contributor Estimation for use with GlobalFiler™ and PowerPlex® Fusion 6c Generated Data. Forensic Science International: Genetics. 43. 102140. 10.1016/j.fsigen.2019.102140.
- Adelman, J.D., Zhao A., Eberst, D.S. and Marciano, M.A. (2019). Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap. Electrophoresis, 0, 1-9. https://doi.org/10.1002/elps.201900060
- Marciano M.A., Williamson V.R. & Adelman J.D. (2018). A Hybrid Approach to Increase the Informedness of CE-based Data Using Locus-Specific Thresholding and Machine Learning. Forensic Science International: Genetics 35, 26-37 https://doi.org/10.1016/j.fsigen.2018.03.017.
- Williamson, V.R, Laris T.M., Romano R. & Marciano, M.A. (2018). Enhanced DNA Mixture Deconvolution of Sexual Offense Samples Using the DEPArray™ System. Forensic Science International: Genetics 34, 265-276). DOI: https://doi.org/10.1016/j.fsigen.2018.03.001
- Marciano M.A.,Panicker S.X., Liddil G.D., Lindgren D. & Sweder K.S. (2018). Development of a Method to Extract Opium Poppy (Papaver somniferum L.) DNA from Heroin. Nature Scientific Reports 8, 2590. doi:10.1038/s41598-018-20996-9
- 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
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.