A MATLAB Package for Gene Selection
School of Computing and Information Sciences
Florida International University
LIBGS is a Matlab package for gene selection.
It includes many popular gene selection methods widely used for expression
data sets and provides a platform to perform performance
NOTE: Currently, this software is under review for publication and it will take a couple of weeks. Thank you for your interest and the software will be open for download upon the completion of the review process.
If you have any problems, please contact
me before you download any file in this site.
Gene Selection Algorithms
the main features selection algorithms are listed as follows:
datasets are provided as following, all of them are fomated by .mat:
- The ALL
dataset covers six subtypes of acute lymphoblastic leukemia: BCR, E2A,
Hyperdip, MLL, T and TEL
- The GCM
dataset consists of 198 human tumor samples of fifteen types
- The HBC
dataset consists of 22 herediray breast cancer samples with three
- The LYM
dataset is of the three most prevalent adult lymphoid malignancies
- The MLL
dataset consists three classes
- The NCI60
Assistant Tools for Classification
To evaluate the performance of gene selection
algorithms by classification accuracy, the Matlab interfaces for two classification
tools are provided as following:
- LIBSVM is an
integrated software for support vector classifiction, (C-SVC, nu-SVC),
regression (spsilon-SVR, nu-SVR) and distribution estimation(one-class
SVM). It supports multi-class classification.
- Weka is a collection
of machine learning algorithms for data mining tasks. The algorithms can
either be applied directly to a dataset or called from your own Java
code. Weka contains tools for data pre-processing, classification,
regression, clustering, association rules, and visualization. It is also
well-suited for developing new machine learning schemes.
Matlab interface for WEKA classification.
Yi Zhang, Dingding Wang and Tao Li. LIBGS: A MATLAB Softeware Package For Gene Selection. International Journal of Data Mining and Bioinformatics, to appear, 2008.
Yi Zhang, Chris Ding and Tao Li. Gene Selection Algorithm by Combining ReliefF and mRMR. BMC Genomics, to appear, 2008.