Big Data Analytics in Chemoinformatics and Bioinformatics

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: - 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 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