ArticleBiology direct2019
Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.
Article in Biology direct, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Topological data analysis captures horizontal gene transfer in antimicrobial resistance gene families among clinically relevant bacteria.Frontiers in microbiology · 2025Article
- Microbiome Geographic Population Structure (mGPS) Detects Fine-Scale Geography.Genome biology and evolution · 2024Article
- CAMDA 2023: Finding patterns in urban microbiomes.Frontiers in genetics · 2024Article
- Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.Scientific reports · 2022Article
- Metagenomic Geolocation Using Read Signatures.Frontiers in genetics · 2022Article
- Recent advances in cancer immunotherapy.Discover oncology · 2021Review
- Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data.Frontiers in genetics · 2021Article
- Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier.Frontiers in genetics · 2021Article
- Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.Biology direct · 2020Article
- Liquid biopsies and cancer omics.Cell death discovery · 2020Review
- A machine learning framework to determine geolocations from metagenomic profiling.Biology direct · 2020Article
- Pathophysiology of Crohn's disease inflammation and recurrence.Biology direct · 2020Review
- Serological determinants of COVID-19.Biology direct · 2020Article
- Cancer predictive studies.Biology direct · 2020Review
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.
resultsHere we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.
conclusionsWe demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances. REVIEWERS: Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.
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