Abstract
Many gene selection algorithms have been applied in gene expression data analysis successfully. To solve different developing environments of these toolkits, such as rankgene (Su et al., 2003), and mRMR (http://research.janelia.org/peng/proj/mrmr/index.htm), perform data analysis and make algorithm comparison more flexible, we have developed a software package LIBGS including: 1) Seven new gene selection algorithms implemented using MATLAB. 2) MATLAB interface for Rankgene. 3) MATLAB interface for LIBSVM and WEKA. 4) Programs for converting data formats. 5) A collection of six popular gene expression data sets. These features make LIBGS a useful tool in gene expression analysis and feature selection.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Data Mining and Bioinformatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
