Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
University of Trento · City University of New York
Abstract
Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbial features presents new challenges, and validated computational tools for learning tasks are lacking. Moreover, classification rules have scarcely been validated in independent studies, posing questions about the generality and generalization of disease-predictive models across cohorts. In this paper, we comprehensively assess approaches to metagenomics-based prediction tasks and for quantitative assessment of the strength of potential microbiome-phenotype…
Citation impact
- FWCI
- 20.89
- Percentile
- 100%
- References
- 75
Authors
5Topics & keywords
- Metagenomics
- Computer science
- Computational biology
- Data science
- Machine learning
- Bioinformatics
- Artificial intelligence
- Biology
Funding
- NSNational Science FoundationAwards: CNS-0855217, CNS-958379, 1126113, ACI-1126113, 0855217
- ECEuropean CommissionAward: 618833
- MDMinistero dell’Istruzione, dell’Università e della RicercaAwards: FIR RBFR13EWWI, RBFR13EWWI
- FCFondazione Cassa Di Risparmio Di Trento E RoveretoAwards: 2013.0239, Rif.Int.2013.0239
- NINational Institute of Allergy and Infectious DiseasesAward: 1R21AI121784-01