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.
1. Data Science for Genomics - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xi 7. Preface - p. xiii 8. Acknowledgment - p. xv 9. Chapter 1 Genomics and neural networks in electrical load forecasting with computational intelligence - p. 1 10. Chapter 2 Application of ensemble learning–based classifiers for genetic expression data classification - p. 11 11. Chapter 3 Machine learning in genomics: identification and modeling of anticancer peptides - p. 25 12. Chapter 4 Genetic factor analysis for an early diagnosis of autism through machine learning - p. 69 13. Chapter 5 Artificial intelligence and data science in pharmacogenomics-based drug discovery: Future of medicines - p. 85 14. Chapter 6 Recent challenges, opportunities, and issues in various data analytics - p. 99 15. Chapter 7 In silico application of data science, genomics, and bioinformatics in screening drug candidates against COVID-19 - p. 107 16. Chapter 8 Toward automated machine learning for genomics: evaluation and comparison of state-of-the-art AutoML approaches - p. 129 17. Chapter 9 Effective dimensionality reduction model with machine learning classification for microarray gene expression data - p. 153 18. Chapter 10 Analysis the structural, electronic and effect of light on PIN photodiode achievement through SILVACO software: a case study - p. 165 19. Chapter 11 One step to enhancement the performance of XGBoost through GSK for prediction ethanol, ethylene, ammonia, acetaldehyde, acetone, and toluene - p. 179 20. Chapter 12 A predictive model for classifying colorectal cancer using principal component analysis - p. 205 21. 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 - p. 217 22. Chapter 14 Predictive analytics of genetic variation in the COVID-19 genome sequence: a data science perspective - p. 229 23. Chapter 15 Genomic privacy: performance analysis, open issues, and future research directions - p. 249 24. Chapter 16 Automated and intelligent systems for next-generation-based smart applications - p. 265 25. Chapter 17 Machine learning applications for COVID-19: a state-of-the-art review - p. 277 26. Index - p. 291
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.
1. Data Science for Genomics - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xi 7. Preface - p. xiii 8. Acknowledgment - p. xv 9. Chapter 1 Genomics and neural networks in electrical load forecasting with computational intelligence - p. 1 10. Chapter 2 Application of ensemble learning–based classifiers for genetic expression data classification - p. 11 11. Chapter 3 Machine learning in genomics: identification and modeling of anticancer peptides - p. 25 12. Chapter 4 Genetic factor analysis for an early diagnosis of autism through machine learning - p. 69 13. Chapter 5 Artificial intelligence and data science in pharmacogenomics-based drug discovery: Future of medicines - p. 85 14. Chapter 6 Recent challenges, opportunities, and issues in various data analytics - p. 99 15. Chapter 7 In silico application of data science, genomics, and bioinformatics in screening drug candidates against COVID-19 - p. 107 16. Chapter 8 Toward automated machine learning for genomics: evaluation and comparison of state-of-the-art AutoML approaches - p. 129 17. Chapter 9 Effective dimensionality reduction model with machine learning classification for microarray gene expression data - p. 153 18. Chapter 10 Analysis the structural, electronic and effect of light on PIN photodiode achievement through SILVACO software: a case study - p. 165 19. Chapter 11 One step to enhancement the performance of XGBoost through GSK for prediction ethanol, ethylene, ammonia, acetaldehyde, acetone, and toluene - p. 179 20. Chapter 12 A predictive model for classifying colorectal cancer using principal component analysis - p. 205 21. 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 - p. 217 22. Chapter 14 Predictive analytics of genetic variation in the COVID-19 genome sequence: a data science perspective - p. 229 23. Chapter 15 Genomic privacy: performance analysis, open issues, and future research directions - p. 249 24. Chapter 16 Automated and intelligent systems for next-generation-based smart applications - p. 265 25. Chapter 17 Machine learning applications for COVID-19: a state-of-the-art review - p. 277 26. Index - p. 291
