Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology provides an up-to-date presentation of big data analytics methods and their applications in diverse fields. The proper management of big data for decision-making in scientific and social issues is of paramount importance. This book gives researchers the tools they need to solve big data problems in these fields. It begins with a section on general topics that all readers will find useful and continues with specific sections covering a range of interdisciplinary applications.
Here, an international team of leading experts review their respective fields and present their latest research findings, with case studies used throughout to analyze and present key information.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xv 7. Preface - p. xix 8. Section 1: General section - p. xxi 9. 1. Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data - p. 3 10. 2. Robustness concerns in high-dimensional data analyses and potential solutions - p. 37 11. 3. Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making - p. 61 12. Section 2: Chemistry & chemoinformatics section - p. 84 13. 4. How to integrate the “small and big” data into a complex adverse outcome pathway? - p. 99 14. 5. Big data and deep learning: extracting and revising chemical knowledge from data - p. 115 15. 6. Retrosynthetic space modeled by big data descriptors - p. 151 16. 7. Approaching history of chemistry through big data on chemical reactions and compounds - p. 171 17. 8. Combinatorial and quantum techniques for large data sets: hypercubes and halocarbons - p. 187 18. 9. Development of quantitative structure–activity relationship models based on electrophilicity index: a conceptual DFT-based descriptor - p. 219 19. 10. Pharmacophore-based virtual screening of large compound databases can aid “big data” problems in drug discovery - p. 231 20. 11. A new robust classifier to detect hot-spots and null-spots in protein–protein interface: validation of binding pocket and identification of inhibitors in in vitro and in vivo models - p. 247 21. 12. Mining big data in drug discovery—triaging and decision trees - p. 265 22. Section 3: Bioinformatics and computatioanl toxicology section - p. 278 23. 13. Use of proteomics data and proteomics-based biodescriptors in the estimation of bioactivity/toxicity of chemicals and nanosubstances - p. 285 24. 14. Mapping interaction between big spaces; active space from protein structure and available chemical space - p. 299 25. 15. Artificial intelligence, big data and machine learning approaches in genome-wide SNP-based prediction for precision medicine and drug discovery - p. 333 26. 16. Applications of alignment-free sequence descriptors in the characterization of sequences in the age of big data: a case study with Zika virus, SARS, MERS, and COVID-19 - p. 359 27. 17. Scalable quantitative structure–activity relationship systems for predictive toxicology - p. 391 28. 18. From big data to complex network: a navigation through the maze of drug–target interaction - p. 407 29. 19. Dissecting big RNA-Seq cancer data using machine learning to find disease-associated genes and the causal mechanism - p. 437 30. Index - p. 455
Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology provides an up-to-date presentation of big data analytics methods and their applications in diverse fields. The proper management of big data for decision-making in scientific and social issues is of paramount importance. This book gives researchers the tools they need to solve big data problems in these fields. It begins with a section on general topics that all readers will find useful and continues with specific sections covering a range of interdisciplinary applications.
Here, an international team of leading experts review their respective fields and present their latest research findings, with case studies used throughout to analyze and present key information.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xv 7. Preface - p. xix 8. Section 1: General section - p. xxi 9. 1. Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data - p. 3 10. 2. Robustness concerns in high-dimensional data analyses and potential solutions - p. 37 11. 3. Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making - p. 61 12. Section 2: Chemistry & chemoinformatics section - p. 84 13. 4. How to integrate the “small and big” data into a complex adverse outcome pathway? - p. 99 14. 5. Big data and deep learning: extracting and revising chemical knowledge from data - p. 115 15. 6. Retrosynthetic space modeled by big data descriptors - p. 151 16. 7. Approaching history of chemistry through big data on chemical reactions and compounds - p. 171 17. 8. Combinatorial and quantum techniques for large data sets: hypercubes and halocarbons - p. 187 18. 9. Development of quantitative structure–activity relationship models based on electrophilicity index: a conceptual DFT-based descriptor - p. 219 19. 10. Pharmacophore-based virtual screening of large compound databases can aid “big data” problems in drug discovery - p. 231 20. 11. A new robust classifier to detect hot-spots and null-spots in protein–protein interface: validation of binding pocket and identification of inhibitors in in vitro and in vivo models - p. 247 21. 12. Mining big data in drug discovery—triaging and decision trees - p. 265 22. Section 3: Bioinformatics and computatioanl toxicology section - p. 278 23. 13. Use of proteomics data and proteomics-based biodescriptors in the estimation of bioactivity/toxicity of chemicals and nanosubstances - p. 285 24. 14. Mapping interaction between big spaces; active space from protein structure and available chemical space - p. 299 25. 15. Artificial intelligence, big data and machine learning approaches in genome-wide SNP-based prediction for precision medicine and drug discovery - p. 333 26. 16. Applications of alignment-free sequence descriptors in the characterization of sequences in the age of big data: a case study with Zika virus, SARS, MERS, and COVID-19 - p. 359 27. 17. Scalable quantitative structure–activity relationship systems for predictive toxicology - p. 391 28. 18. From big data to complex network: a navigation through the maze of drug–target interaction - p. 407 29. 19. Dissecting big RNA-Seq cancer data using machine learning to find disease-associated genes and the causal mechanism - p. 437 30. Index - p. 455
