Bioinformatics and Medical Applications

Book description

BIOINFORMATICS AND MEDICAL APPLICATIONS

The main topics addressed in this book are big data analytics problems in bioinformatics research such as microarray data analysis, sequence analysis, genomics-based analytics, disease network analysis, techniques for big data analytics, and health information technology.

Bioinformatics and Medical Applications: Big Data Using Deep Learning Algorithms analyses massive biological datasets using computational approaches and the latest cutting-edge technologies to capture and interpret biological data. The book delivers various bioinformatics computational methods used to identify diseases at an early stage by assembling cutting-edge resources into a single collection designed to enlighten the reader on topics focusing on computer science, mathematics, and biology. In modern biology and medicine, bioinformatics is critical for data management. This book explains the bioinformatician’s important tools and examines how they are used to evaluate biological data and advance disease knowledge.

The editors have curated a distinguished group of perceptive and concise chapters that presents the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to healthcare. Applying deep learning techniques for data-driven solutions in health information allows automated analysis whose method can be more advantageous in supporting the problems arising from medical and health-related information.

Audience

The primary audience for the book includes specialists, researchers, postgraduates, designers, experts, and engineers, who are occupied with biometric research and security-related issues.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. 1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction
    1. 1.1 Introduction
    2. 1.2 Literature Review
    3. 1.3 Tools and Techniques
    4. 1.4 Proposed Method
    5. 1.5 Conclusion
    6. References
  6. 2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach
    1. 2.1 Introduction
    2. 2.2 Literature Review
    3. 2.3 Proposed Systems
    4. 2.4 Experimental Results and Analysis
    5. 2.5 Novelties
    6. 2.6 Future Scope, Limitations, and Possible Applications
    7. 2.7 Recommendations and Consideration
    8. 2.8 Conclusions
    9. References
  7. 3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case
    1. 3.1 Introduction
    2. 3.2 Human Coronavirus Types
    3. 3.3 The SARS-CoV-2 Pandemic Impact
    4. 3.4 Computational Predictors
    5. 3.6 Future Implications
    6. 3.7 Acknowledgments
    7. References
  8. 4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations
    1. 4.1 Introduction
    2. 4.2 Background
    3. 4.3 Related Works
    4. 4.4 Methods
    5. 4.5 Conclusion and Future Work
    6. 4.6 Acknowledgments
    7. References
  9. 5 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health
    1. 5.1 Introduction
    2. 5.2 Types of Biological Networks
    3. 5.3 Methodologies in Network Embedding
    4. 5.4 Attributed and Non-Attributed Network Embedding
    5. 5.5 Applications of Network Embedding in Computational Biology
    6. 5.6 Limitations of Network Embedding in Biology
    7. 5.7 Conclusion and Outlook
    8. References
  10. 6 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier
    1. 6.1 Introduction
    2. 6.2 Related Study
    3. 6.3 Methodology
    4. 6.4 Implementation and Result Analysis
    5. 6.5 Conclusion
    6. References
  11. 7 Deep Learning for Medical Informatics and Public Health
    1. 7.1 Introduction
    2. 7.2 Deep Learning Techniques in Medical Informatics and Public Health
    3. 7.3 Applications of Deep Learning in Medical Informatics and Public Health
    4. 7.4 Open Issues Concerning DL in Medical Informatics and Public Health
    5. 7.5 Conclusion
    6. References
  12. 8 An Insight Into Human Pose Estimation and Its Applications
    1. 8.1 Foundations of Human Pose Estimation
    2. 8.2 Challenges to Human Pose Estimation
    3. 8.3 Analyzing the Dimensions
    4. 8.4 Standard Datasets for Human Pose Estimation
    5. 8.5 Deep Learning Revolutionizing Pose Estimation
    6. 8.6 Application of Human Pose Estimation in Medical Domains
    7. 8.7 Conclusion
    8. References
  13. 9 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare
    1. 9.1 Introduction
    2. 9.2 Literature Survey
    3. 9.3 System Design and Methodology
    4. 9.4 Novelty in Our Work
    5. 9.5 Future Scope, Possible Applications, and Limitations
    6. 9.6 Recommendations and Consideration
    7. 9.7 Conclusions
    8. References
  14. 10 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century
    1. 10.1 Introduction
    2. 10.2 Literature Survey
    3. 10.3 The Methodology and Protocols Followed
    4. 10.4 Experimental Setup of an Experiment
    5. 10.5 Results and Discussions
  15. 11 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment
    1. 11.1 Introduction
    2. 11.2 Literature Survey
    3. 11.3 Proposed Work
    4. 11.4 Analysis of the Work
    5. 11.5 Conclusion
    6. References
  16. 12 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach
    1. 12.1 Introduction
    2. 12.2 Literature Survey
    3. 12.3 Methodology
    4. 12.4 Results and Discussion
    5. 12.5 Interpretations and Analysis
    6. 12.6 Novelty in Our Work
    7. 12.7 Recommendations
    8. 12.8 Future Scope and Possible Applications
    9. 12.9 Limitations
    10. 12.10 Conclusions
    11. 12.11 Acknowledgments
    12. References
  17. 13 Collection and Analysis of Big Data From Emerging Technologies in Healthcare
    1. 13.1 Introduction
    2. 13.2 Data Collection
    3. 13.3 Data Analysis
    4. 13.4 Research Trends
    5. 13.5 Conclusion
    6. References
  18. 14 A Complete Overview of Sign Language Recognition and Translation Systems
    1. 14.1 Introduction
    2. 14.2 Sign Language Recognition
    3. 14.3 Dataset Creation
    4. 14.4 Hardware Employed for Sign Language Recognition
    5. 14.5 Computer Vision–Based Sign Language Recognition and Translation Systems
    6. 14.6 Sign Language Translation System—A Brief Overview
    7. 14.7 Conclusion
    8. References
  19. Index
  20. End User License Agreement

Product information

  • Title: Bioinformatics and Medical Applications
  • Author(s): A. Suresh, S. Vimal, Y. Harold Robinson, Dhinesh Kumar Ramaswami, R. Udendhran
  • Release date: April 2022
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119791836