图书简介
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.
GENERAL TOPICS 1. Neural Net, Genetic Algorithm and Pattern Recognition in Big Data Analysis 2. Robustness Concerns in High-dimensional Data Analysis and Potential Solutions 3. The Social Face of Big Data: Privacy, Transparency, Bias and Fairness in Algorithms SPECIFIC AREAS: CHEMISTRY AND CHEMOINFORMATICS 4. Chemistry by Discrete Math and Numbers: Structure Characterization and Property / Bioactivity/ Toxicity Prediction 5. Integrating of Data into a Complex Adverse Outcome Pathway 6. Big Data and Deep Learning: Extracting and Revising Chemical Knowledge from Data 7. Retrosynthetic Space Persuaded by Big Data Descriptors 8. Approaching History of Chemistry through Big Data on Chemical Reactions and Compounds 9. Quantum Molecular Dynamics, Topological, Group Theoretical and Graph Theoretical Studies of Protein-Protein Interactions 10. Development of QSAR / QSPR / QSTR Models Based on Conceptual DFT Based Reactivity Descriptors 11. Pharmacophore Based Virtual Screening of Large Compound Databases Can Aid "Big Data" Problems in Drug Discovery 12. Druggability Assessment of the Hot-Spots in the Protein-Protein Interface Using Machine Learning Algorithms 13. Multi-modal Classification and Fuzzy Logic Techniques in the Analysis of Large Data Sets in Drug Discovery BIOINFORMATICS AND COMPUTATIONAL TOXICOLOGY 14. Use of Proteomics Data and Proteomics Based Biodescriptors in the Estimation Of Bioactivity/ Toxicity of Chemicals 15. Mapping Interaction between Big spaces; Active Space from Protein Structure and Available Chemical Space 16. Big Data, AI and Machine Learning Approaches in Genome-wide SNP Based Prediction for Precision Medicine and Drug Discovery 17. Dissecting Big RNA-sequence Cancer Database Using Machine Learning Tool to Find Disease-Associated Genes and the Causal Mechanism of Disease 18. Mathematical Sequence Descriptors in the Characterization of Emerging Global Pathogens: A Case Study with the Zika Virus 19. Scalable QSAR Systems for Predictive Toxicology 20. Network Models for Describing Disparate Big Data for Proteins: From Sequence and Structures to Interactions
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