Big Data Analytics and Deep Learning in Bioinformatics With Hadoop

Abstract

Abstract Bioinformatics research is regarded as an area which encompasses voluminous, expanding and complex datasets. Nowadays, with the use of high-throughput next-generation sequencing technologies, there is significant expansion of biological big data, which presents storage and processing challenges. Performing data analytics to harvest the wealth of data from biological and biomedical data, such as genetic mapping on the DNA sequence, will only help to advance our understanding of the human condition, health and disease; which will consequently allow curing diseases and improving human health and lives by supporting the development of precision methods for healthcare. In this chapter, big data analytics with regards to the Hadoop big data framework for storing and processing big data is described in the context of bioinformatics. Moreover, machine learning is an important approach for performing predictive and prescriptive analytics. Thus, machine learning and deep learning approaches currently being used in the context of big data analytics in the Hadoop framework are also presented, as well as the current uses of such techniques and tools in bioinformatics.