Data Science for Genomics

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes.
Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR.

[*] Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics
[*] Presents a roadmap of future trends suitable for innovative Data Science research and practice
[*] Includes topics such as Blockchain technology for securing data at end user/server side
[*] Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns

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