Microarray Filtering-Based Fuzzy C-Means Clustering and Classification in Genomic Signal Processing

Genomic signal processing is a development field in medicine and agriculture. Numerous research areas are processing the genomics of living organism such as animals and particularly human beings. In this paper, the microarray data set for the biological organism which includes a large number of gene data has taken for the processing. The microarray data are a powerful technology practised in the research field for validating the gene discovery and diagnosis of diseases. The data are processed to a large number with plenty of genes. The proposed Kalman filter-based fuzzy c-means cluster and artificial neural network (KF-FANN) enhance the genomic signal processing to the optimal level. The Kalman filter proposed in this paper to remove the noise and smoothen the data for signal processing. An ideal clustering process is carried out for the classification of the microarray data. The fuzzy c-means clustering was proposed in this paper for grouping the microarray after removing the noise. The artificial neural network is a biologically inspired model proposed in this work for the classification of microarray data to point out the normal and abnormal genes in the microarray data. The proposed work has compared with existing techniques such as c-means, k-means clustering, and multi-SVM, respectively. The proposed method is carried out in the MATLAB platform, and results are evaluated in terms of Calinski-Harabasz index, separation index, Xie and Beni's index, partition index, accuracy, precision, recall, and F-score. The analysed result shows that the proposed KF-FANN is an efficient method for the classification of microarray data than existing approaches in genomic signal processing.