title: A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval.
abstract: Research on the human microbiome, the microbiota that live in, on, and around the human person, has revolutionized our understanding of the complex interactions between microbial life and human health and disease. The microbiome may also provide a valuable tool in forensic death investigations by helping to reveal the postmortem interval (PMI) of a decedent that is discovered after an unknown amount of time since death. Current methods of estimating PMI for cadavers discovered in uncontrolled, unstudied environments have substantial limitations, some of which may be overcome through the use of microbial indicators. In this project, we sampled the microbiomes of decomposing human cadavers, focusing on the skin microbiota found in the nasal and ear canals. We then developed several models of statistical regression to establish an algorithm for predicting the PMI of microbial samples. We found that the complete data set, rather than a curated list of indicator species, was preferred for training the regressor. We further found that genus and family, rather than species, are the most informative taxonomic levels. Finally, we developed a k-nearest- neighbor regressor, tuned with the entire data set from all nasal and ear samples, that predicts the PMI of unknown samples with an average error of ±55 accumulated degree days (ADD). This study outlines a machine learning approach for the use of necrobiome data in the prediction of the PMI and thereby provides a successful proof-of- concept that skin microbiota is a promising tool in forensic death investigations.
authors.affiliation: Department of Mathematics and Computer Science, John Jay College, The City University of New York, New York, NY, United States of America 10019.
authors.forename: Hunter R
authors.initials: HR
authors.lastname: Johnson
authors.affiliation: Department of Sciences, John Jay College, The City University of New York, New York, NY, United States of America 10019.
authors.forename: Donovan D
authors.initials: DD
authors.lastname: Trinidad
authors.affiliation: Department of Sciences, John Jay College, The City University of New York, New York, NY, United States of America 10019.
authors.forename: Stephania
authors.initials: S
authors.lastname: Guzman
authors.affiliation: Department of Mathematics and Computer Science, John Jay College, The City University of New York, New York, NY, United States of America 10019.|Department of Sciences, John Jay College, The City University of New York, New York, NY, United States of America 10019.
authors.forename: Zenab
authors.initials: Z
authors.lastname: Khan
authors.affiliation: Department of Sciences, John Jay College, The City University of New York, New York, NY, United States of America 10019.
authors.forename: James V
authors.initials: JV
authors.lastname: Parziale
authors.affiliation: Department of Biosystems Engineering & Soil Science, University of Tennessee, Knoxville, TN, United States of America, 37996.
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: DeBruyn
authors.affiliation: Department of Sciences, John Jay College, The City University of New York, New York, NY, United States of America 10019.
authors.forename: Nathan H
authors.identifier: http://orcid.org/0000-0003-1509-6415
authors.initials: NH
authors.lastname: Lents
mesh_terms: D001419:Bacteria / Q000235:genetics / Q000302:isolation & purification; D002102:Cadaver; D004269:DNA, Bacterial / Q000235:genetics / Q000378:metabolism; D049429:Forensic Pathology; D006801:Humans; D000069550:Machine Learning; D064307:Microbiota; D011180:Postmortem Changes; D012867:Skin / Q000382:microbiology*; D013997:Time Factors
chemical_list: D004269:DNA, Bacterial