Santos Kumar Baliarsingh1, Swati Vipsita2, Amir H Gandomi3, Abhijeet Panda4, Sambit Bakshi5, Somula Ramasubbareddy6. 1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India. Electronic address: santos.baliarsinghfcs@kiit.ac.in. 2. Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, India. Electronic address: swati@iiit-bh.ac.in. 3. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia. Electronic address: gandomi@uts.edu.au. 4. International Institute of Information Technology, Hyderabad 500032, India. Electronic address: abhijeet.panda@students.iiit.ac.in. 5. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India. Electronic address: sambitbaksi@gmail.com. 6. VNR VJIET, Hyderabad, Telangana 500090, India. Electronic address: svramasubbareddy1219@gmail.com.
Abstract
BACKGROUND: The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods. METHODS: In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN. RESULTS: These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification. CONCLUSION: The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis. SIGNIFICANCE: Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.
BACKGROUND: The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods. METHODS: In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN. RESULTS: These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification. CONCLUSION: The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis. SIGNIFICANCE: Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.
Authors: R Rathipriya; Abdul Aziz Abdul Rahman; S Dhamodharavadhani; Abdelrhman Meero; G Yoganandan Journal: Neural Comput Appl Date: 2022-10-06 Impact factor: 5.102