Cover for Data Science for Genomics

Data Science for Genomics

Book2023

Edited by:

Amit Kumar Tyagi and Ajith Abraham

Data Science for Genomics

Book2023

 

Cover for Data Science for Genomics

Edited by:

Amit Kumar Tyagi and Ajith Abraham

About the book

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Book description

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeli ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Genomics and neural networks in electrical load forecasting with computational intelligence

    Prasannavenkatesan Theerthagiri

    Pages 1-10

  3. Book chapterAbstract only

    Chapter 2 - Application of ensemble learning–based classifiers for genetic expression data classification

    Saumendra Kumar Mohapatra, Abhishek Das and Mihir Narayan Mohanty

    Pages 11-23

  4. Book chapterAbstract only

    Chapter 3 - Machine learning in genomics: identification and modeling of anticancer peptides

    Girish Kumar Adari, Maheswari Raja and P. Vijaya

    Pages 25-68

  5. Book chapterAbstract only

    Chapter 4 - Genetic factor analysis for an early diagnosis of autism through machine learning

    A. Chaitanya Kumar, J. Andrew John, ... P. Vijaya

    Pages 69-84

  6. Book chapterAbstract only

    Chapter 5 - Artificial intelligence and data science in pharmacogenomics-based drug discovery: Future of medicines

    Vikas Jhawat, Sumeet Gupta, ... Anroop Nair

    Pages 85-97

  7. Book chapterAbstract only

    Chapter 6 - Recent challenges, opportunities, and issues in various data analytics

    Kannadhasan Suriyan and Nagarajan Ramalingam

    Pages 99-105

  8. Book chapterAbstract only

    Chapter 7 - In silico application of data science, genomics, and bioinformatics in screening drug candidates against COVID-19

    Rene Barbie Browne, Jai Narain Vishwakarma, ... Jayanti Datta Roy

    Pages 107-128

  9. Book chapterAbstract only

    Chapter 8 - Toward automated machine learning for genomics: evaluation and comparison of state-of-the-art AutoML approaches

    Akbar Ali Khan, Prakriti Dwivedi, ... Gulshan Soni

    Pages 129-152

  10. Book chapterAbstract only

    Chapter 9 - Effective dimensionality reduction model with machine learning classification for microarray gene expression data

    Yakub Kayode Saheed

    Pages 153-164

  11. Book chapterAbstract only

    Chapter 10 - Analysis the structural, electronic and effect of light on PIN photodiode achievement through SILVACO software: a case study

    Samaher Al-Janabi, Ihab Al-Janabi and Noora Al-Janabi

    Pages 165-178

  12. Book chapterAbstract only

    Chapter 11 - One step to enhancement the performance of XGBoost through GSK for prediction ethanol, ethylene, ammonia, acetaldehyde, acetone, and toluene

    Samaher Al-Janabi, Hadeer Majed and Saif Mahmood

    Pages 179-203

  13. Book chapterAbstract only

    Chapter 12 - A predictive model for classifying colorectal cancer using principal component analysis

    Micheal Olaolu Arowolo, Happiness Eric Aigbogun, ... Amit Kumar Tyagi

    Pages 205-216

  14. Book chapterAbstract only

    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

    Olalekan J. Awujoola, Francisca N. Ogwueleka, ... Olayinka R. Adelegan

    Pages 217-228

  15. Book chapterAbstract only

    Chapter 14 - Predictive analytics of genetic variation in the COVID-19 genome sequence: a data science perspective

    V. Kakulapati, S. Mahender Reddy, ... Sriman Naini

    Pages 229-247

  16. Book chapterAbstract only

    Chapter 15 - Genomic privacy: performance analysis, open issues, and future research directions

    M. Shamila, K. Vinuthna and Amit Kumar Tyagi

    Pages 249-263

  17. Book chapterAbstract only

    Chapter 16 - Automated and intelligent systems for next-generation-based smart applications

    H.R. Deekshetha and Amit Kumar Tyagi

    Pages 265-276

  18. Book chapterAbstract only

    Chapter 17 - Machine learning applications for COVID-19: a state-of-the-art review

    Firuz Kamalov, Aswani Kumar Cherukuri, ... Akbar Hossain

    Pages 277-289

  19. Book chapterNo access

    Index

    Pages 291-296

About the book

Description

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.

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.

Key Features

  • 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
  • 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

Details

ISBN

978-0-323-98352-5

Language

English

Published

2023

Copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Imprint

Academic Press

Editors

Amit Kumar Tyagi

Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India

Ajith Abraham

Director, Machine Intelligence Research Labs, United States