Supervised Machine Learning Enables Geospatial Microbial Provenance

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applica...

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Bibliographic Details
Main Authors: Chandrima Bhattacharya, Braden T. Tierney, Krista A. Ryon, Malay Bhattacharyya, Jaden J. A. Hastings, Srijani Basu, Bodhisatwa Bhattacharya, Debneel Bagchi, Somsubhro Mukherjee, Lu Wang, Elizabeth M. Henaff, Christopher E. Mason
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Genes
Subjects:
microbial fingerprint
microbial forensics
supervised machine learning
metagenomics
bioindicator species
Online Access:https://www.mdpi.com/2073-4425/13/10/1914

Internet

https://www.mdpi.com/2073-4425/13/10/1914

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