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Independent component analysis (ICA) can find hidden information on the mass spectrometry (MS) data. However, ICA does not take advantage of prior information in the construction of sub-space, as no consideration is taken about the class information. In this research a supervised version of ICA (SICA) is introduced. Due to the large amount of information contained within MS data, the 'curse of dimensionality' must be solved before ICA and SICA are employed. This paper examines the performance of ICA and SICA using the following feature extraction and feature selection algorithms on ovarian cancer MS data, namely principal component analysis (PCA), 2nd-PCA, and T-test. Experimental results show that the performance of ICA and SICA can achieve good classification results on ovarian cancer MS dataset pre-processed by T-test.