The non-negative matrix factorization toolbox for biological data mining

Source Code for Biology and Medicine

Alioune Ngom¹  Yifeng Li¹ 

[1] School of Computer Science, University of Windsor, Windsor, Ontario, Canada

关键词: Missing values;    Classification;    Feature selection;    Feature extraction;    Bi-clustering;    Clustering;    Non-negative matrix factorization;   

Others  :  805727
DOI  :  10.1186/1751-0473-8-10

 received in 2012-11-30, accepted in 2013-04-10,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Li and Ngom; licensee BioMed Central Ltd.

【 图 表 】

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