Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology

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. Brings together the current knowledge on the most important aspects of big data, including analysis using deep learning and fuzzy logic, transparency and data protection, disparate data analytics, and scalability of the big data domain Covers many applications of big data analysis in diverse fields such as chemistry, chemoinformatics, bioinformatics, computer-assisted drug/vaccine design, characterization of emerging pathogens, and environmental protection Highlights the considerable benefits offered by big data analytics to science, in biomedical fields and in industry INDICE: GENERAL SECTION: CHEMOINFORMATICS AND BIOINFORMATICS BY DISCRETE MATHEMATICS AND NUMBERS: An adventure from small data to the realm of emerging big data Robustness Concerns in High-dimensional Data Analysis and Potential Solutions, The Social Face of Big Data: Privacy, Transparency, Bias and Fairness in Algorithms CHEMISTRY & CHEMOINFORMATICS SECTION: How to integrate the 'small and big' data into a complex adverse outcome pathway Big data and deep learning: extracting and revising chemical knowledge from data Retrosynthetic space persuades by big data descriptors Approaching history of chemistry through big data on chemical reactions and compounds Combinatorial Techniques for Large Data Sets: Hypercubes and Halocarbons Development of QSAR/QSPR/QSTR models based on Electrophilicity index: A Conceptual DFT based descriptor Pharmacophore based virtual screening of large compound databases can aid big data problems in drug discovery. 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 Mining Big Data in Drug Discovery - Triaging and Decision Trees BIOINFORMATICS AND COMPUTATIOANL TOXICOLOGY SECTION: Use of proteomics data and proteomics based biodescriptors in the estimation of bioactivity/ toxicity of chemicals and nanosubstances Mapping Interaction between Big spaces; active space from Protein structure and available chemical space Artificial Intelligence, Big Data and Machine Learning approaches in Genome-wide SNP based prediction for Precision Medicine & Drug Discovery Applications of alignment-free sequence descriptors (AFSDs) in the characterization of sequences in the age of big data: A case study with Zika virus, SARS, MERS, and COVID-19 Scalable QSAR Systems for Predictive Toxicology From big data to complex network: a navigation through the maze of drug-target interaction Dissecting big RNA-Seq cancer data using machine learning to find disease-associated genes and the causal mechanism

• ISBN: 978-0-323-85713-0
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 466
• Fecha Publicación: 01/08/2022
• Nº Volúmenes: 1
• Idioma: Inglés