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