title: The non-negative matrix factorization toolbox for biological data mining.
abstract: BACKGROUND
  Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data. Though there currently exists packages implemented in R and other programming languages, they either provide only a few optimization algorithms or focus on a specific application field. There does not exist a complete NMF package for the bioinformatics community, and in order to perform various data mining tasks on biological data.
  
  
  RESULTS
  We provide a convenient MATLAB toolbox containing both the implementations of various NMF techniques and a variety of NMF-based data mining approaches for analyzing biological data. Data mining approaches implemented within the toolbox include data clustering and bi-clustering, feature extraction and selection, sample classification, missing values imputation, data visualization, and statistical comparison.
  
  
  CONCLUSIONS
  A series of analysis such as molecular pattern discovery, biological process identification, dimension reduction, disease prediction, visualization, and statistical comparison can be performed using this toolbox.
authors.affiliation: School of Computer Science, University of Windsor, Windsor, Ontario, Canada. li11112c@uwindsor.ca.
authors.forename: Yifeng
authors.initials: Y
authors.lastname: Li
authors.forename: Alioune
authors.initials: A
authors.lastname: Ngom