A New Orthogonal Discriminant Projection Based Prediction Method for Bioinformatic Data

DNA microarray allows the measurement of transcript abundances for thousands of genes in parallel. Though, it is an important procedure to select informative genes related to tumor from those gene expression profiles (GEP) because of its characteristics such as high dimensionality, small sample set and many noises. In this paper we proposed a novel method for feature extraction that is named as Orthogonal Discriminant Projection (ODP). This method is a linear approximation base on manifold learning approach. The ODP method characterizes the local and non-local information of manifold distributed data and explores an optimum subspace which can maximize the difference between non-local scatter and the local scatter. Moreover, it introduces the class information to enhance the recognition ability. A trick has been employed to handle the Small Sample Site (SSS). Experimental results on Non-small Cell Lung Cancer (NSCLC) and glioma dataset validates its efficiency compared to other widely used dimensionality reduction methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA).

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