Tumor Disease Multiclass Prediction using Biomolecular Gene Expression Data by Signal Processing and Computational Intelligence Techniques

Abstract

Tumor disease multiclass prediction from nucleotide expression is an emerging research area in the field of bioinformatics. Gene expression profiling has been emerged as an efficient technique for cancer classification as well as for diagnosis, prognosis, and treatment purposes. Studying cancer microarray gene expression data is a challenging task because microarray is high dimensional dataset with a noisy data. Efficient feature extraction and computational method development is indispensible for the analysis. In this paper a feature extraction method by Discrete Cosine Transform (DCT) and discrete wavelet transform (DWT) has been proposed to detect informative genes effectively. DCT offers a dimension reduction in feature sets. Again the approximation coefficients obtained by the decomposition at a level in DWT is used as the features for further study. Then K-means algorithm is applied on optimized feature datasets to cluster. These cluster information are feature sets and classified using Back-Propagation Neural Network (BPNN) classifier to efficiently predict the class. The potential of the proposed approach is validated by many benchmark datasets such as lungs cancer dataset, breast cancer data set, Prostate cancer dataset, brain tumor dataset, colon dataset, Leukemia dataset. The experimental results show that the proposed method can be a useful approach for cancer classification with low computational complexity and high accuracy.