Applications of PCA-Based Unsupervised FE to Bioinformatics

Although PCA is often blamed as an old technology, if it is useful, no other reasons will be required to be used. In this chapter, I will apply PCA-based unsupervised FE to various bioinformatics problems. As discussed in the earlier chapter, PCA-based unsupervised FE is fitted to the situation that there are more number of features than the number of samples. This specific situation is very usual because features are genes whose numbers are as many as several tens thousands, while the number of samples is as many as that of patients, which is often as many as a few tens. The application of PCA-based unsupervised FE ranges from biomarker identification and identification of disease causing genes to in silico drug discovery. I try to mention studies where PCA-based unsupervised FE is applied as many as possible, from the published papers by myself.