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Structural and Functional Data Processing in Bio-Computing and Deep Learning
Abstract
The goal of new biocomputing research is to comprehend bio molecules' structures and functions via the lens of biofuturistic technologies. The amount of data generated every day is tremendous, and data bases are growing exponentially. A majority of computational researchers have been using machine learning for the analysis of bio-informatics data sets. This chapter explores the relationship between deep learning algorithms and the fundamental biological concepts of protein structure, phenotypes and genotype, proteins and protein levels, and the similarities and differences between popular deep learning models. This chapter offers a useful outlook for further research into its theory, algorithms, and applications in computational biology and bioinformatics. Understanding the structural aspects of cellular contact networks helps to comprehend the interdependencies, causal chains, and fundamental functional capabilities that exist across the entire network.
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