A Review of Feature Extraction Software for Microarray Gene Expression Data

Table 4

Summary of ICA software.

Number	Software	Author/year	Language	Features
1	FastICA	Marchini et al. [18]	R and MATLAB	ICA algorithm is provided for implementing the analysis using ICA
2	JADE	Nordhausen et al. [19]	R	(i) JADE algorithm is provided for ICA; (ii) Other BSS methods such as AMUSE and SOBI are offered
3	HiPerSAT	Keith et al. [20]	C++, MATLAB, and EEGLAB	(i) Integration of FastICA, Informax, and SOBI algorithms; (ii) Data whitening is provided
4	MineICA	Biton et al. [21]	R	(i) Storage and visualization of ICA results; (ii) Annotation of features
5	Pearson ICA	Karnanen [22]	R	Extraction of the independent components using the minimization of mutual information from the Pearson system
6	Maximum Likelihood ICA	Teschenforff [23]	R	Implementation of the Maximum Likelihood and fixed-point algorithm into ICA