Microbiome Tools for Forensic Science

Trends in Biotechnology

Volumn 35, Issue 9, 2017, Pages 814-823

  Metcalf, Jessica L ^a   Xu, Zhenjiang Z ^b   Bouslimani, Amina ^c   Dorrestein, Pieter ^b,c,d,e   Carter, David O ^f   Knight, Rob ^b,e,g  

^a Department of Environmental and Radiological Health Sciences   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c San Diego   (United States)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^e UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^f CHAMINADE UNIVERSITY OF HONOLULU   (United States)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

forensics; machine learning; metabolomic; microbiome; postmortem interval; trace evidence

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACTERIA; CRIME; DATA INTEGRATION; DNA SEQUENCES; FORENSIC SCIENCE; GENE ENCODING; LEARNING SYSTEMS; THROUGHPUT;

FORENSICS; METABOLOMICS; MICROBIOME; POST MORTEM INTERVALS; TRACE EVIDENCES;

LEARNING ALGORITHMS;

INTERNAL TRANSCRIBED SPACER; RNA 16S; RNA 18S;

16SRRNA GENE; 18SRRNA GENE; ALGOR MORTIS; AMPLICON; AUTOPSY; BODY TEMPERATURE; FORENSIC SCIENCE; GAS CHROMATOGRAPHY; GENE SEQUENCE; HUMAN; LIQUID CHROMATOGRAPHY; MACHINE LEARNING; METABOLOMICS; MICROBIAL COMMUNITY; MICROBIAL DIVERSITY; NONHUMAN; POSTMORTEM INTERVAL; PRIORITY JOURNAL; REVIEW; RIGOR MORTIS; SKIN FLORA; SYSTEMS BIOLOGY; TANDEM MASS SPECTROMETRY; ALGORITHM; ANIMAL; HIGH THROUGHPUT SEQUENCING; MICROFLORA; PROCEDURES; TRENDS;

ALGORITHMS; ANIMALS; FORENSIC SCIENCES; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MACHINE LEARNING; MICROBIOTA;

EID: 85016505810     PISSN: 01677799     EISSN: 18793096     Source Type: Journal    
DOI: 10.1016/j.tibtech.2017.03.006     Document Type: Review

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