title: Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.
abstract: BACKGROUND
  Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota.
  
  
  RESULTS
  By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data.
  
  
  BACKGROUND
  The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/.
  
  
  BACKGROUND
  zliu@umm.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Epidemiology and Public Health, University of Maryland Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA. zliu@umm.edu
authors: Zhenqiu
authors: Z
authors: Liu
authors: William
authors: W
authors: Hsiao
authors: Brandi L
authors: BL
authors: Cantarel
authors: Elliott Franco
authors: EF
authors: Drábek
authors: Claire
authors: C
authors: Fraser-Liggett
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001419:Bacteria / Q000145:classification*; D006801:Humans; D054892:Metagenome; D056186:Metagenomics / Q000379:methods*; D018401:Sample Size; D012867:Skin / Q000382:microbiology