Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters. Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS. This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework: Bayesian in Clinical Research and Sample Size Calcuation Bayesian in Time-to-Event Data Analysis Bayesian in Longitudinal Data Analysis Bayesian in Diagnostics Test Statistics This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.

Alzheimer's disease, one of the most rapidly growing neurodegenerative disorders, is characterized by a progressive loss of memory. Despite several advances in the field of medical therapeutics, a viable treatment for Alzheimer's disease would be of great importance. Medicinal plants represent a largely untapped reservoir of natural medicines and potential sources of anti-Alzheimer’s drugs. The structural diversity of their phytoconstituents makes these plants a valuable source of novel lead compounds in the quest for drugs to treat Alzheimer's disease. Based on traditional literature and up-to-date research, various new therapeutically active compounds have been identified from phytoextracts, which could be useful in the treatment of cognitive disorders. Phytomedicine and Alzheimer’s Disease presents information on Mechanistic aspects of neurodegeneration in Alzheimer’s disease and the role of phytochemicals as restorative agents Understanding the complex biochemical aspects of Alzheimer’s disease Pre-clinical approaches to evaluating drugs to target Alzheimer’s disease Assessing alternative approaches to treating Alzheimer’s disease and the role of alternative medicine to delay the symptomatic progression of this disease Epigenetic changes in Alzheimer’s disease and possible therapeutic or dietary interventions This book serves as an excellent resource for scientific investigators, academics, biochemists, botanists, and alternative medicine practitioners who work to advance the role of phytomedicines in treating Alzheimer’s disease.

Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area. Features: Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.

The book reviews the recent research advances and their outcomes in the areas of structural biology, bioinformatics, phytochemistry and drug discovery. Chapters in the book cover multidisciplinary research to understand the molecular mechanisms involved in protein-protein/ligand interactions. It employs an integrative approach to identify the therapeutic targets for HIV, and cancer, pathogen and viral infection pathways and the identification of their potential drug candidates. The book also provides examples of computational molecular dynamics simulations to understand the conformational changes in the molecules. Some chapters are focused on exploring potent bioactive compounds from natural sources.This book can serve as a single source that covers several interdisciplinary research fields which will be beneficial to Researchers and students in postgraduate studies.

Bayesian: The Ultimate Choice of Clinical Trial GCP and ICH-9 are appropriate choice to be considered for any Clinical Trial. However, it is tedious procedure to be conduct. The major issue in any Clinical Trial is sample size.The application of sample sample size is suitable to handle the issue.The Bayesian is ultimate choice to be accepted for trial having small sample size.This book is illustrated with Bayesian approach in longitudinal data analysis.