computational and statistical methods for protein quantification by mass spectrometry

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Computational And Statistical Methods For Protein Quantification By Mass Spectrometry

Author : Ingvar Eidhammer
ISBN : 9781118493779
Genre : Mathematics
File Size : 60. 34 MB
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The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

Computational And Statistical Methods For Protein Quantification By Mass Spectrometry

Author : Ingvar Eidhammer
ISBN : 9781118493786
Genre : Mathematics
File Size : 90. 33 MB
Format : PDF, ePub
Download : 903
Read : 1325

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The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

Protein Bioinformatics

Author : Ingvar Eidhammer
ISBN : UOM:39015058724942
Genre : Mathematics
File Size : 54. 2 MB
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Pairwise global alignment of sequences. Pairwise local alignment and database search. Statical analysis. Multiple global alignment and phylogenetic trees. Scoring matrices. Profiles. Sequence patterns. Structures and structure descriptions. Superposition and Dynamic programming. Geometric techniques. Clustering: Combining local similarities. Significance and assessment of structure comparisons. Multiple structure comparison. Protein structure classification. Structure prediction: Threading. Basics in mathematics, probability and algorithms. Introduction to molecular biology.

Introduction To Linear Regression Analysis

Author : Douglas C. Montgomery
ISBN : 9780470542811
Genre : Mathematics
File Size : 73. 24 MB
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"This book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research"--

Proteome Bioinformatics

Author : Simon J. Hubbard
ISBN : 1617796808
Genre : Computers
File Size : 52. 49 MB
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The feld of proteomics moves rapidly. New methods, techniques, applications, standards, models and software appear almost on a daily basis. Accompanying this are plenty of texts on the experimental side of the feld and a few appearing on the informatic and data analysis side. This latterly includes one in the Methods in Molecular Biology series tackling the specifc analysis of “Mass spectrometry data in proteomics” in MMB vol. 376. This current collection builds on this, but takes a broader view of proteome data analysis covering data analysis essentials, but also the databases and data models, as well as practical consid- ations for analysing database search results, annotating genomes, and speeding up searches. It also digs deeper into some topics, such as decoy database searching and aspects of signal processing in proteomic mass spectrometry. The aim of the volume is to provide the reader with a mix of reviews and methodology chapters, which build from the essentials of database searching in proteomics, on through specifc data processing challenges to databases, data standards and data models.

A First Course In Probability And Markov Chains

Author : Giuseppe Modica
ISBN : 9781118477748
Genre : Mathematics
File Size : 77. 22 MB
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Provides an introduction to basic structures of probability with a view towards applications in information technology A First Course in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main areas. The first part explores notions and structures in probability, including combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as weak and strong laws of large numbers and central limit theorem. In the second part of the book, focus is given to Discrete Time Discrete Markov Chains which is addressed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains. This book also looks at making use of measure theory notations that unify all the presentation, in particular avoiding the separate treatment of continuous and discrete distributions. A First Course in Probability and Markov Chains: Presents the basic elements of probability. Explores elementary probability with combinatorics, uniform probability, the inclusion-exclusion principle, independence and convergence of random variables. Features applications of Law of Large Numbers. Introduces Bernoulli and Poisson processes as well as discrete and continuous time Markov Chains with discrete states. Includes illustrations and examples throughout, along with solutions to problems featured in this book. The authors present a unified and comprehensive overview of probability and Markov Chains aimed at educating engineers working with probability and statistics as well as advanced undergraduate students in sciences and engineering with a basic background in mathematical analysis and linear algebra.

Bioinformatics Methods In Clinical Research

Author : Rune Matthiesen
ISBN : UOM:39015075696222
Genre : Science
File Size : 72. 25 MB
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Integrated bioinformatics solutions have become increasingly valuable in past years, as technological advances have allowed researchers to consider the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes, and as the costs of such techniques have begun to lessen. In Bioinformatics Methods in Clinical Research, experts examine the latest developments impacting clinical omics, and describe in great detail the algorithms that are currently used in publicly available software tools. Chapters discuss statistics, algorithms, automated methods of data retrieval, and experimental consideration in genomics, transcriptomics, proteomics, and metabolomics. Composed in the highly successful Methods in Molecular BiologyTM series format, each chapter contains a brief introduction, provides practical examples illustrating methods, results, and conclusions from data mining strategies wherever possible, and includes a Notes section which shares tips on troubleshooting and avoiding known pitfalls. Informative and ground-breaking, Bioinformatics Methods in Clinical Research establishes a much-needed bridge between theory and practice, making it an indispensable resource for bioinformatics researchers.

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