The Non-negative Matrix Factorization Toolbox for Biological Data Mining

Overview

Journal Source Code Biol Med

Publisher Biomed Central

Specialty Biology

Date 2013 Apr 18

PMID 23591137

Citations 41

Authors

Yifeng Li

Alioune Ngom

Affiliations

Soon will be listed here.

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.

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