Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier.

ABSTRACT: Microbiome samples harvested from urban environments can be informative in predicting the geographic location of unknown samples. The idea that different cities may have geographically disparate microbial signatures can be utilized to predict the geographical location based on city-specific microbiome samples. We implemented this idea first; by utilizing standard bioinformatics procedures to pre-process the raw metagenomics samples provided by the CAMDA organizers. We trained several component classifiers and a robust ensemble classifier with data generated from taxonomy-dependent and taxonomy-free approaches. Also, we implemented class weighting and an optimal oversampling technique to overcome the class imbalance in the primary data. In each instance, we observed that the component class

SUBMITTER: Anyaso-Samuel S 

PROVIDER: S-EPMC8093763 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature