Conference proceeding
FORENSIC IDENTIFICATION WITH ENVIRONMENTAL SAMPLES
2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), pp 1861-1864
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The field of forensics aims to understand the physical biomarkers that make each person unique. Recently, it has been discovered that one of the traits that makes us unique from one another are the composition of the microbial communities found throughout our bodies. For example, identical twins who share the same set of DNA may have vastly different microbial communities in or on various body sites. It was recently discovered that microbial communities can be exploited for forensic identification by clustering samples from individual's skin and objects that they may have previously touched. Typically, this is done by using basic multi-dimensional scaling analysis using phylogenetic distances. In this work, we circumvent the use of phylogenetic distances by using the raw community abundances, and we present an application of kernels for metagenomic data analysis. In addition, we show that strategic selection of features can improve classification accuracy.
Metrics
Details
- Title
- FORENSIC IDENTIFICATION WITH ENVIRONMENTAL SAMPLES
- Creators
- Gregory Ditzler - Drexel UniversityGail Rosen - Drexel UniversityRobi Polikar - Rowan UniversityIEEE
- Publication Details
- 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), pp 1861-1864
- Series
- International Conference on Acoustics Speech and Signal Processing ICASSP
- Publisher
- IEEE
- Number of pages
- 4
- Grant note
- SC004335 / Department of Energy; United States Department of Energy (DOE) 0845827; 1120622; ECCS-0926159 / National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000312381401239
- Scopus ID
- 2-s2.0-84867606932
- Other Identifier
- 991019170558904721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Acoustics
- Engineering, Electrical & Electronic