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Big Data Analytics in Chemoinformatics and Bioinformatics
With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology
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809 g
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229x152x23 mm
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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).is senior researcher at Kemijski Institut/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
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
- Integrating 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, by Claudiu N Lungu
- 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 Models
- Mining Big Data in Drug Discovery - Triaging and Decision Trees
- 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
CHEMISTRY & CHEMOINFORMATICS SECTION:
BIOINFORMATICS AND COMPUTATIOANL TOXICOLOGY SECTION:
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.