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Data Science for Genomics
- 1st Edition - November 27, 2022
- Editors: Amit Kumar Tyagi, Ajith Abraham
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 8 3 5 2 - 5
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 8 5 7 6 - 5
Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and model… Read more
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Request a sales quoteData 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
Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science and Engineering, Biomedical Engineering, Biology, Chemistry, Genomics, and Information Technology. The audience also includes interested professionals-experts from both public and private industries of biomedical, genomics, computer science, data science, and information technology; The book may be used in Data Science, Medical, Biomedical, Artificial Intelligence, Machine Learning, Deep Learning oriented courses given at especially Health, Biology, Biomedical Engineering, Genetics or similar programs of universities, institutions
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Acknowledgment
- 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
- No. of pages: 312
- Language: English
- Edition: 1
- Published: November 27, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323983525
- eBook ISBN: 9780323985765
AT
Amit Kumar Tyagi
Dr. Amit Kumar Tyagi is Assistant Professor and Senior Researcher at Vellore Institute of Technology (VIT), Chennai, India. His current research focuses on Machine Learning with Big Data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart and Secure Computing and Privacy. He has contributed to several projects such as “AARIN” and “P3-Block” to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems. He received his Ph.D. Degree from Pondicherry Central University, India. He is a member of IEEE.
Affiliations and expertise
Assistant Professor, at National Institute of Fashion Technology, New Delhi, IndiaAA
Ajith Abraham
Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.
Affiliations and expertise
Pro Vice-Chancellor, Sai University, Chennai, IndiaRead Data Science for Genomics on ScienceDirect