(Ebook PDF) Data Science for Genomics 1st Edition by Amit Kumar Tyagi, Ajith Abraham ISBN 9780323985765 0323985769 full chapters

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

• 1. Introduction
• 2. Methodology
• 3. Experiment evaluation
• 4. Conclusion

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

• 1. Introduction
• 2. Ensemble learning–based classifiers for genetic data classification
• 3. Stacked ensemble classifier for leukemia classification
• 4. Results and discussion
• 5. Conclusion

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

• 1. Introduction
• 2. Materials and methods

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

• 1. Introduction
• 2. Review of literature
• 3. Methodology
• 4. Results
• 5. Conclusion
• Appendix

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

• 1. Introduction
• 2. Artificial intelligence
• 3. Artificial intelligence in drug research
• 4. Drug discovery
• 5. Pharmacogenomics
• 6. Pharmacogenomics and AI
• 7. Integration of pharmacogenomics and AI
• 8. Pharmacogenomic-based clinical evaluation and AI
• 9. Discussion
• 10. Conclusion
• Abbreviations

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

• 1. Introduction
• 2. Big data
• 3. Data analytics
• 4. Challenges in data analytics
• 5. Various sectors in data analytics
• 6. Conclusion

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

• 1. Introduction
• 2. Materials and method
• 3. Results and discussion
• 4. Conclusion
• Declaration
• Nomenclature

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

• 1. Into the world of genomics
• 2. Need and purpose of analytics in genomics
• 3. Literature review
• 4. Research design
• 5. AutoML
• 6. Research outcome
• 7. Business implications
• 8. Conclusion

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

• 1. Introduction
• 2. Related work
• 3. Materials and methods
• 4. Results and discussion
• 5. Conclusion and future work

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

• 1. Introduction
• 2. PIN photodiode [23,24]
• 3. Results and simulations
• 4. Conclusion
• Appendix (Silvaco Code)

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

• 1. Introduction
• 2. Related work
• 3. Main tools
• 4. Result of implementation
• 5. Conclusions

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

• 1. Introduction
• 2. Related works
• 3. Methodology
• 4. Results and discussions
• 5. Conclusion

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

• 1. Introduction
• 2. Review of literature
• 3. Materials and methods
• 4. Result and discussion
• 5. Conclusion and future work

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

• 1. Introduction
• 2. Related work
• 3. The COVID-19 genomic sequence
• 4. Methodology
• 5. Discussion
• 6. Conclusion
• 7. Future outlook

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

• 1. Introduction
• 2. Related work
• 3. Motivation
• 4. Importance of genomic data/privacy in real life
• 5. Techniques for protecting genetic privacy
• 6. Genomic privacy: use case
• 7. Challenges in protecting genomic data
• 8. Opportunities in genomic data privacy
• 9. Arguments about genetic privacy with several other privacy areas
• 10. Conclusion with future scope
• Appendix A

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

• 1. Introduction
• 2. Background work
• 3. Intelligent systems for smart applications
• 4. Automated systems for smart applications
• 5. Automated and intelligent systems for smart applications
• 6. Machine learning and AI technologies for smart applications
• 7. Analytics for advancements
• 8. Cloud strategies: hybrid, containerization, serverless, microservices
• 9. Edge intelligence
• 10. Data governance and quality for smart applications
• 11. Digital Ops including DataOps, AIOps, and CloudSecOps
• 12. AI in healthcare—from data to intelligence
• 13. Big data analytics in IoT-based smart applications
• 14. Big data applications in a smart city
• 15. Big data intelligence for cyber-physical systems
• 16. Big data science solutions for real-life applications
• 17. Big data analytics for cybersecurity and privacy
• 18. Data analytics for privacy-by-design in smart health
• 19. Case studies and innovative applications
• 20. Conclusion and future scope

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

• 1. Introduction
• 2. Forecasting
• 3. Medical diagnostics
• 4. Drug development
• 5. Contact tracing
• 6. Conclusion
• Index