An efficient gene bigdata analysis using machine learning algorithms

• Published in: Multimedia tools and applications Vol. 79; no. 15-16; pp. 9847 - 9870
• Main Authors: Wang, Ge, Pu, Pengbo, Shen, Tingyan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Big Data; Bioinformatics; Computer Communication Networks; Computer Science; Convolution; Data analysis; Data Structures and Information Theory; Genomes; Machine learning; Mathematical analysis; MicroRNAs; Multimedia Information Systems; Special Purpose and Application-Based Systems; Big data analytics; Machine learning algorithms; Gene big data; Pattern recognition; Bioinformatics; Prediction
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08358-7

Bioinformatics is one of the emerging and rapidly developing research areas that is predominantly used for genetic data analysis and processing. Bioinformatics is characterized by its huge and voluminous data that is growing in nature which in turn complicates data analysis. In most cases, Bioinformatics data analysis and processing involve big data analytics due to the complex nature of the data. Previous research works handled data analytics using traditional tools and conventional big data analytical methods. However, it can be proved that machine learning algorithms and approaches can be effectively deployed to perform parallel, distributed and incremental processing of complex big data analytics especially in the case of gene big data analytics to enhance the efficiency in processing this large chunk of Bioinformatics-based gene big data. This paper provides a Machine Learning algorithm-based Convolution Neural Network (ML-CNN) approach for the process of identifying potential target genes, predicting miRNAs, visualizing the unique miRNA patterns, and validating genomes. The proposed approach has experimented with MATLAB software using deep learning toolbox on the pre - miRNA dataset. Experimental results indicate that machine learning algorithms certainly increases the efficiency of Bioinformatics-based methods of processing gene data in terms of prediction accuracy and reduced processing time. The mean performance of ML-CNN is improved 7% high than the existing system.