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 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

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  • Author : Julie Thompson
  • Publisher : Elsevier
  • Pages : 146 pages
  • ISBN : 0081019610
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKStatistics for Bioinformatics

Statistics for Bioinformatics

Statistics for Bioinformatics
  • Author : Julie Thompson
  • Publisher : Elsevier
  • Release : 24 November 2016
GET THIS BOOKStatistics 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 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

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
  • Author : Warren J. Ewens,Gregory R. Grant
  • Publisher : Springer Science & Business Media
  • Release : 30 March 2006
GET THIS BOOKStatistical 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 methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community. This book provides an introduction to some of these new methods. The main

Statistical Bioinformatics

Statistical Bioinformatics
  • Author : Jae K. Lee
  • Publisher : John Wiley & Sons
  • Release : 20 September 2011
GET THIS BOOKStatistical 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 of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and

Statistical Bioinformatics with R

Statistical Bioinformatics with R
  • Author : Sunil K. Mathur
  • Publisher : Academic Press
  • Release : 21 December 2009
GET THIS BOOKStatistical 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 theory in the context of bioinformatics applications. The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
  • Author : Warren J. Ewens,Gregory R. Grant
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOKStatistical 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.

Basics of Bioinformatics

Basics of Bioinformatics
  • Author : Rui Jiang,Xuegong Zhang,Michael Q. Zhang
  • Publisher : Springer Science & Business Media
  • Release : 26 November 2013
GET THIS BOOKBasics of Bioinformatics

This book outlines 11 courses and 15 research topics in bioinformatics, based on curriculums and talks in a graduate summer school on bioinformatics that was held in Tsinghua University. The courses include: Basics for Bioinformatics, Basic Statistics for Bioinformatics, Topics in Computational Genomics, Statistical Methods in Bioinformatics, Algorithms in Computational Biology, Multivariate Statistical Methods in Bioinformatics Research, Association Analysis for Human Diseases: Methods and Examples, Data Mining and Knowledge Discovery Methods with Case Examples, Applied Bioinformatics Tools, Foundations for the Study of

Handbook of Statistical Bioinformatics

Handbook of Statistical Bioinformatics
  • Author : Henry Horng-Shing Lu,Bernhard Schölkopf,Hongyu Zhao
  • Publisher : Springer Science & Business Media
  • Release : 17 May 2011
GET THIS BOOKHandbook 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 and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments

Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics
  • Author : Dipak K. Dey,Samiran Ghosh,Bani K. Mallick
  • Publisher : CRC Press
  • Release : 03 September 2010
GET THIS BOOKBayesian 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

Statistical Methods in Bioinformatics

Statistical Methods in Bioinformatics
  • Author : W. Warren John Ewens,Gregory Robert Grant
  • Publisher : Springer Science & Business Media
  • Release : 18 September 2021
GET THIS BOOKStatistical Methods in Bioinformatics

Probability theory (i): one random variable. Probability theory (ii); many random variables. Statistics (i): an introduction to statistical inference. Stochastic processes (i): poisson processes and markov chains. Stochastic processes (iii): markov chains. Hidden markov models. Computationally intensive methods. Evolutionary models. Phylogenetic tree estimation. Basic notions in biology. C computational aspects of the binominal and generalized geometric distribution functions. D BLAST: sums of normalized scores. References. Author index. Index.

Introduction to Bioinformatics with R

Introduction to Bioinformatics with R
  • Author : Edward Curry
  • Publisher : CRC Press
  • Release : 02 November 2020
GET THIS BOOKIntroduction 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 Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the

R Programming for Bioinformatics

R Programming for Bioinformatics
  • Author : Robert Gentleman
  • Publisher : CRC Press
  • Release : 14 July 2008
GET THIS BOOKR Programming for Bioinformatics

Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems. Drawing on the author’s first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming

Bayesian Methods in Structural Bioinformatics

Bayesian Methods in Structural Bioinformatics
  • Author : Thomas Hamelryck,Kanti Mardia,Jesper Ferkinghoff-Borg
  • Publisher : Springer
  • Release : 23 March 2012
GET THIS BOOKBayesian 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 in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal

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

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications
  • Author : K. G. Srinivasa,G. M. Siddesh,S. R. Manisekhar
  • Publisher : Springer Nature
  • Release : 30 January 2020
GET THIS BOOKStatistical 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 supplements, gene therapy, proteomics and agriculture.

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Bioinformatics and Computational Biology Solutions Using R and Bioconductor
  • Author : Robert Gentleman,Vincent Carey,Wolfgang Huber,Rafael Irizarry,Sandrine Dudoit
  • Publisher : Springer Science & Business Media
  • Release : 27 January 2006
GET THIS BOOKBioinformatics 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 that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.