Trends and Application of Data Science in Bioinformatics

Abstract

AbstractAdvancement of sequencing technologies, rapid advances in omics generated an extensive volume of biological data in recent years. It requires sophisticated analytical tools to analyze and draw conclusions from such massive amount of data. Bioinformatics is an inter-disciplinary science of analyzing and interpreting biological data by application of statistics, computational methodologies, and information technology. As huge volume of genomic, proteomic, and other data is generated, analysis and interpretation of such biological data sets involves use of data science and data mining tools. Hence, researchers are required to rely increasingly on data-science tools to store and analyze the data. Data science is an inter-disciplinary science that uses algorithms and scientific methods to derive information and insights from the big data. Data science extracts scientific work out of a wide variety of subjects viz., computer science, mathematics, statistics, databases, machine learning and optimization, etc. These strategies promote investigation and advancement of innovative methods to improve the incorporation of big data and data science into biological research. Advancements in computing and data science offers viable analytical techniques for processing huge biological data. Consequently, there is a huge possibility to enhance the interaction between bioinformatics and data science. Future applications of data science should concentrate on creating high-end integrated technologies for relatively low-cost processing of enormous biological data, greater efficiency, and reliable protection measures to advance bioinformatics research.KeywordsBig dataBioinformaticsData miningDeep learningMachine learningCloud computing