Data Science for Genomics

Amit Kumar Tyagi, Ajith Abraham

• Publisher: Academic Press
• Year: 2022
• Print ISBN: 9780323983525
• eBook ISBN: 9780323985765
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

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

Table of contents

• Data Science for Genomics
• Cover
• Title Page
• Copyright
• Table of Contents
• Contributors
• Preface
• Acknowledgment
• Chapter 1 Genomics and neural networks in electrical load forecasting with computational intelligence
• Chapter 2 Application of ensemble learning–based classifiers for genetic expression data classification
• Chapter 3 Machine learning in genomics: identification and modeling of anticancer peptides
• Chapter 4 Genetic factor analysis for an early diagnosis of autism through machine learning
• Chapter 5 Artificial intelligence and data science in pharmacogenomics-based drug discovery: Future of medicines
• Chapter 6 Recent challenges, opportunities, and issues in various data analytics
• Chapter 7 In silico application of data science, genomics, and bioinformatics in screening drug candidates against COVID-19
• Chapter 8 Toward automated machine learning for genomics: evaluation and comparison of state-of-the-art AutoML approaches
• Chapter 9 Effective dimensionality reduction model with machine learning classification for microarray gene expression data
• Chapter 10 Analysis the structural, electronic and effect of light on PIN photodiode achievement through SILVACO software: a case study
• Chapter 11 One step to enhancement the performance of XGBoost through GSK for prediction ethanol, ethylene, ammonia, acetaldehyde, acetone, and toluene
• Chapter 12 A predictive model for classifying colorectal cancer using principal component analysis
• Chapter 13 Genomic data science systems of Prediction and prevention of pneumonia from chest X-ray images using a two-channel dual-stream convolutional neural network
• Chapter 14 Predictive analytics of genetic variation in the COVID-19 genome sequence: a data science perspective
• Chapter 15 Genomic privacy: performance analysis, open issues, and future research directions
• Chapter 16 Automated and intelligent systems for next-generation-based smart applications
• Chapter 17 Machine learning applications for COVID-19: a state-of-the-art review
• Index