Neal Grantham

Works (5)

Updated: July 5th, 2023 15:40

2020 journal article

Global forensic geolocation with deep neural networks

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 69(4), 909–929.

By: N. Grantham n, B. Reich n , E. Laber n, K. Pacifici n , R. Dunn n , N. Fierer *, M. Gebert*, J. Allwood n, S. Faith n

author keywords: Citizen science; Machine learning; Microbiome; Non-homogeneous Poisson process; Spatial point pattern
TL;DR: The DeepSpace algorithm makes remarkably good point predictions; for example, when applied to the microbiomes of over 1300 dust samples collected across continental USA, more than half of geolocated predictions produced by this model fall less than 100 km from their true origin, which is a 60% reduction in error from competing geolocation methods. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: July 6, 2020

2020 journal article

Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance

FORENSIC SCIENCE INTERNATIONAL, 310.

By: J. Allwood n, N. Fierer *, R. Dunn n , M. Breen n , B. Reich n , E. Laber n, J. Clifton n, N. Grantham n, S. Faith n

author keywords: Forensic microbiology; Bioinformatics; Metabarcoding; Sample provenance
MeSH headings : DNA Barcoding, Taxonomic; DNA, Fungal / chemistry; Demography; Forensic Sciences; Fungi; Humans; Reference Values; Soil
TL;DR: Investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs to facilitate a standardized method for consistent, reproducible sample treatment. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: May 26, 2020

2019 journal article

MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(530), 599–609.

By: N. Grantham n, Y. Guan n, B. Reich n , E. Borer * & K. Gross n

author keywords: Continuous shrinkage prior; Factor analysis; Microbiome; Mixed model; Nutrient Network; OTU abundance data
TL;DR: A novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model the authors call microbiome mixed model (MIMIX), tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: July 22, 2019

2018 journal article

Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter

Environmetrics, 29(4).

By: N. Grantham, B. Reich , Y. Liu & H. Chang

Source: NC State University Libraries
Added: August 6, 2018

2015 journal article

Fungi Identify the Geographic Origin of Dust Samples

PLOS ONE, 10(4), e0122605.

By: N. Grantham n, B. Reich n , K. Pacifici n , E. Laber n, H. Menninger n, J. Henley *, A. Barberán *, J. Leff*, N. Fierer *, R. Dunn n 

Contributors: N. Grantham n, B. Reich n , K. Pacifici n , E. Laber n, H. Menninger n, J. Henley *, A. Barberán *, J. Leff*, N. Fierer *, R. Dunn n 

Ed(s): A. Rokas

MeSH headings : Archaeology; Dust; Fungi / classification; Fungi / genetics; Genetic Variation; Pollen / genetics; Pollen / microbiology
TL;DR: A statistical learning algorithm via discriminant analysis is developed that exploits this geographic endemicity in the fungal diversity of dust samples to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, ORCID, Crossref
Added: August 6, 2018

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