A Review of Feature Extraction Software for Microarray Gene Expression Data

Table 11

Related work.

Software	Author	Motivation	Advantage
RDRToolbox	Bartenhagen [35]	(i) To reduce high dimensionality microarray data; (ii) To preserve most of the significant information and generate data with similar characteristics like the high-dimensional original	(i) Combine information from all features; (ii) Suited for low-dimensional representations of the whole data
Scikit-learn	Pedregosa et al. [36]	To calculate activity index parameters through clustering	(i) Easy-to-use interface; (ii) Can easily be integrated into applications outside the traditional range of statistical data analysis
lle	Diedrich and Abel [34]	Currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex	(i) Fast; (ii) Purely unsupervised