Computation in BioInformatics

Computation in BioInformatics

by S. Balamurugan, Anand T. Krishnan, Dinesh Goyal, Balakumar Chandrasekaran, Boomi Pandi
Released October 2021
Publisher(s): Wiley-Scrivener
ISBN: 9781119654711

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Book description

COMPUTATION IN BIOINFORMATICS

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design.

The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development.

Audience

Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. 1 Bioinfomatics as a Tool in Drug Designing
    1. 1.1 Introduction
    2. 1.2 Steps Involved in Drug Designing
    3. 1.3 Various Softwares Used in the Steps of Drug Designing
    4. 1.4 Applications
    5. 1.5 Conclusion
    6. References
  6. 2 New Strategies in Drug Discovery
    1. 2.1 Introduction
    2. 2.2 Road Toward Advancement
    3. 2.3 Methodology
    4. 2.4 Role of OMICS Technology
    5. 2.5 High-Throughput Screening and Its Tools
    6. 2.6 Chemoinformatic
    7. 2.7 Concluding Remarks and Future Prospects
    8. References
  7. 3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective
    1. 3.1 Introduction
    2. 3.2 Bioinformatics and Drug Discovery
    3. 3.3 Bioinformatics Tools in Early Drug Discovery
    4. 3.4 Future Directions With Bioinformatics Tool
    5. 3.5 Conclusion
    6. Acknowledgements
    7. References
  8. 4 Role of Data Mining in Bioinformatics
    1. 4.1 Introduction
    2. 4.2 Data Mining Methods/Techniques
    3. 4.3 DNA Data Analysis
    4. 4.4 RNA Data Analysis
    5. 4.5 Protein Data Analysis
    6. 4.6 Biomedical Data Analysis
    7. 4.7 Conclusion and Future Prospects
    8. References
  9. 5 In Silico Protein Design and Virtual Screening
    1. 5.1 Introduction
    2. 5.2 Virtual Screening Process
    3. 5.3 Machine Learning and Scoring Functions
    4. 5.4 Conclusion and Future Prospects
    5. References
  10. 6 New Bioinformatics Platform-Based Approach for Drug Design
    1. 6.1 Introduction
    2. 6.2 Platform-Based Approach and Regulatory Perspective
    3. 6.3 Bioinformatics Tools and Computer-Aided Drug Design
    4. 6.4 Target Identification
    5. 6.5 Target Validation
    6. 6.6 Lead Identification and Optimization
    7. 6.7 High-Throughput Methods (HTM)
    8. 6.8 Conclusion and Future Prospects
    9. References
  11. 7 Bioinformatics and Its Application Areas
    1. 7.1 Introduction
    2. 7.2 Review of Bioinformatics
    3. 7.3 Bioinformatics Applications in Different Areas
    4. 7.4 Conclusion
    5. References
  12. 8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression
    1. 8.1 Introduction
    2. 8.2 Data Processing
    3. 8.3 Normalization of Microarray Data Using the RMA Method
    4. 8.4 Statistical Analysis for Differential Gene Expression
    5. 8.5 Conclusion
    6. References
  13. 9 Machine Learning in Bioinformatics
    1. 9.1 Introduction and Background
    2. 9.2 Machine Learning Applications in Bioinformatics
    3. 9.3 Machine Learning Approaches
    4. 9.4 Conclusion and Closing Remarks
    5. References
  14. 10 DNA-RNA Barcoding and Gene Sequencing
    1. 10.1 Introduction
    2. 10.2 RNA
    3. 10.3 DNA Barcoding
    4. 10.4 Main Reasons of DNA Barcoding
    5. 10.5 Limitations/Restrictions of DNA Barcoding
    6. 10.6 RNA Barcoding
    7. 10.7 Methodology
    8. 10.8 Conclusion
    9. Abbreviations
    10. Acknowledgement
    11. References
  15. 11 Bioinformatics in Cancer Detection
    1. 11.1 Introduction
    2. 11.2 The Era of Bioinformatics in Cancer
    3. 11.3 Aid in Cancer Research via NCI
    4. 11.4 Application of Big Data in Developing Precision Medicine
    5. 11.5 Historical Perspective and Development
    6. 11.6 Bioinformatics-Based Approaches in the Study of Cancer
    7. 11.7 Conclusion and Future Challenges
    8. References
  16. 12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression
    1. 12.1 Introduction
    2. 12.2 FSHR Gene
    3. 12.3 IL-10 Gene
    4. 12.4 IRS-1 Gene
    5. 12.5 PCR Primers Used
    6. 12.6 Statistical Analysis
    7. 12.7 Conclusion
    8. References
  17. 13 An Insight of Protein Structure Predictions Using Homology Modeling
    1. 13.1 Introduction
    2. 13.2 Homology Modeling Approach
    3. 13.3 Steps Involved in Homology Modeling
    4. 13.4 Tools Used for Homology Modeling
    5. Acknowledgement
    6. References
  18. 14 Basic Concepts in Proteomics and Applications
    1. 14.1 Introduction
    2. 14.2 Challenges on Proteomics
    3. 14.3 Proteomics Based on Gel
    4. 14.4 Non-Gel–Based Electrophoresis Method
    5. 14.5 Chromatography
    6. 14.6 Proteomics Based on Peptides
    7. 14.7 Stable Isotopic Labeling
    8. 14.8 Data Mining and Informatics
    9. 14.9 Applications of Proteomics
    10. 14.10 Future Scope
    11. 14.11 Conclusion
    12. References
  19. 15 Prospects of Covalent Approaches in Drug Discovery: An Overview
    1. 15.1 Introduction
    2. 15.2 Covalent Inhibitors Against the Biological Target
    3. 15.3 Application of Physical Chemistry Concepts in Drug Designing
    4. 15.4 Docking Methodologies—An Overview
    5. 15.5 Importance of Covalent Targets
    6. 15.6 Recent Framework on the Existing Docking Protocols
    7. 15.7 SN2 Reactions in the Computational Approaches
    8. 15.8 Other Crucial Factors to Consider in the Covalent Docking
    9. 15.9 QM/MM Approaches
    10. 15.10 Conclusion and Remarks
    11. Acknowledgements
    12. References
  20. Index
  21. Also of Interest
  22. End User License Agreement

Product information

  • Title: Computation in BioInformatics
  • Author(s): S. Balamurugan, Anand T. Krishnan, Dinesh Goyal, Balakumar Chandrasekaran, Boomi Pandi
  • Release date: October 2021
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119654711