|
Showing 1 - 5 of
5 matches in All Departments
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.
|
You may like...
Suits: Season 3
Rick Hoffman, Gina Torres, …
Blu-ray disc
R51
Discovery Miles 510
|