Chapter 9 - Integrative omics approach for identification of genes associated with disease

Abstract

In recent decade, omics technologies such as proteomics and metabolomics are playing an important role in everyday biological research. Omics technologies mainly focus on multiomics-based data integration methods. Compared with single omics data, multiomics data provide important opportunities in understanding the flow of information, which is a fundamental aspect of diseases. Today in the age of big data, omics data is available in the form of genome, proteome, transcriptome, and metabolomics. In addition to the single omics data type, integrated omics approach can be used to access predictive data for disease analysis in future. High-throughput technologies for genomics, transcriptomics, proteomics, metabolomics, and integrative analysis of these data enable new, systems-level insights into disease pathogenesis. The heterogenous and high-dimensional natures of omics data create many obstacles when performing analysis. Various computational-based approaches such as machine learning, deep learning, data mining, statistical methods, and metaheuristic techniques have attracted significant attention to process, integrate, and analyze omics data. This chapter mainly discusses the present approaches used in integrative omics data analysis mainly based on disease prediction, recurrence, survival, and biomarker discovery. This chapter also examines the comparison and classification of various existing tools and technologies based on common features for the integration and analysis of multiomics data. In addition, important research obstacles and future direction in omics data analysis are also discussed in this chapter. This chapter shows the path of researchers to understand the use of computational-based approaches for efficient omics data analysis.