Big Data Analytics in Chemoinformatics and Bioinformatics

Big Data Analytics in Chemoinformatics and Bioinformatics

With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology

1st Edition - August 1, 2022

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  • Editors: Subhash Basak, Marjan Vračko
  • Paperback ISBN: 9780323857130

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Description

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.

Key Features

  • 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

Readership

Researchers involved in the management and practical use of big data in chemistry, biology, chemoinformatics, bioinformatics, computational chemistry, new drug discovery, drug design, and surveillance of emerging pathogens. Students and young researchers interested in techniques and applications of big data analytics

Table of Contents

  • 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

Product details

  • No. of pages: 466
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: August 1, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780323857130

About the Editors

Subhash Basak

Dr. Basak is an adjunct professor in the department of chemistry and department of biochemistry & molecular biology at the University of Minnesota Duluth. He received his Ph.D. in Biochemistry from the University of Calcutta in 1980. He is a member of several academic societies, including International Society for Mathematical Chemistry, of which he is President, and is a US Chair in the organization of thirteen international mathematical chemistry workshops in USA, South America, and various universities/ research institutes in India. He was awarded ARA Journal Best Paper Award, American Romanian Academy of Arts and Sciences (2004) and Statistics in Chemistry Award, American Statistical Association (2004).

Affiliations and Expertise

University of Minnesota Duluth, USA

Marjan Vračko

is senior researcher at Kemijski Inštitut/National Institute of Chemistry in Ljubljana, Slovenia. Since 1994 his research has been focused on QSAR (quantitative structure-activity relationship) modelling of biological/toxical properties of compounds, to quantum chemistry, to chemometrics (numerical analysis of proteomic and genomic data) and to modeling of interaction between receptors and molecules. He obtained his PhD (1990) from University of Erlangen, FR Germany in the field of quantum chemistry. Later on, he was a post doc at the Columbia University of New York and at the University of Namur, BE (Faculté Universitaire Notre Dame de la Paix, Namur). In 1994 he joined the National Institute of Chemistry in Ljubljana. In 2005 he was senior visiting researcher at the Joint Research Centre of European Commission, Ispra where he worked on applications of (Q)SAR methods for regulatory purposes. He is author of 85 scientific papers and chapters

Affiliations and Expertise

National Institute of Chemistry, Ljubljana, Slovenia

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