statistics for bioinformatics

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Statistics for Bioinformatics
Author :
Publisher : Elsevier
Release Date :
ISBN 10 : 0081019610
Pages : 146 pages
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Statistics for Bioinformatics: Methods for Multiple Sequence Alignment provides an in-depth introduction to the most widely used methods and software in the bioinformatics field. With the ever increasing flood of sequence information from genome sequencing projects, multiple sequence alignment has become one of the cornerstones of bioinformatics. Multiple sequence alignments are crucial for genome annotation, as well as the subsequent structural, functional, and evolutionary studies of genes and gene products. Consequently, there has been renewed interest in the development of novel multiple sequence alignment algorithms and more efficient programs. Explains the dynamics that animate health systems Explores tracks to build sustainable and equal architecture of health systems Examines the advantages and disadvantages of the different approaches to care integration and the management of health information

Statistics for Bioinformatics

Statistics for Bioinformatics: Methods for Multiple Sequence Alignment provides an in-depth introduction to the most widely used methods and software in the bioinformatics field. With the ever increasing flood of sequence information from genome sequencing projects, multiple sequence alignment has become one of the cornerstones of bioinformatics. Multiple sequence alignments

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Modern Statistics for Modern Biology

A far-reaching course in practical advanced statistics for biologists using R/Bioconductor, data exploration, and simulation.

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Statistical Bioinformatics with R

Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications. Designed for a one or two semester senior undergraduate or graduate bioinformatics course, the text takes a broad view of the subject – not just gene expression and sequence analysis, but a careful balance of statistical

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Statistical Bioinformatics

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description

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Statistical Methods in Bioinformatics

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation

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Statistical Methods in Bioinformatics

There was a real need for a book that introduces statistics and probability as they apply to bioinformatics. This book presents an accessible introduction to elementary probability and statistics and describes the main statistical applications in the field.

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Handbook of Statistical Bioinformatics

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c

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Bayesian Methods in Structural Bioinformatics

This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation

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Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations

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Statistical Modelling in Biostatistics and Bioinformatics

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied

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Advances in Statistical Bioinformatics

"Chapter 1 An introduction to next-generation biological platforms Virginia Mohlere, Wenting Wang, and Ganiraju Manyam The University of Texas. MD Anderson Cancer Center 1.1 Introduction When Sanger and Coulson first described a reliable, efficient method for DNA sequencing in 1975 (Sanger and Coulson, 1975), they made possible the full sequencing of both genes and

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques  Tools  and Applications

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health

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Introduction to Bioinformatics with R

In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for

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Applied Computational Biology and Statistics in Biotechnology and Bioinformatics

The book entitled "Applied Computational Biology and Statistics in Biotechnology and Bioinformatics" is aimed to cater to the growing demand of academia, researchers and commercial ventures. Altogether there are forty four chapters divided into the following broad sections like 1. Bioinformatics, Genomics and Proteomics, 2. Phylogeny 3. Drug Design and Epigenomics 4. Advanced Computational

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