Related books on 'sequence-analysis'Return to life science book list
Number of products: 39499
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by: Ethan D. Bloch
publisher: Springer, published: 2011-05-19
sales rank: 825012
This text is a rigorous, detailed introduction to real analysis that presents the fundamentals with clear exposition and carefully written definitions, theorems, and proofs. It is organized in a distinctive, flexible way that would make it equally appropriate to undergraduate mathematics majors who want to continue in mathematics, and to future mathematics teachers who want to understand the theory behind calculus.
The Real Numbers and Real Analysis will serve as an excellent one-semester text for undergraduates majoring in mathematics, and for students in mathematics education who want a thorough understanding of the theory behind the real number system and calculus.
by: Alan Dennis
publisher: Wiley, published: 2007-03-16
sales rank: 194994
You can’t truly understand Systems Analysis and Design (SAD) by only reading about it; you have to do it. In Systems Analysis and Design, Third Edition, Dennis, Wixom, and Roth offer a hands-on approach to actually doing SAD. Building on their experience as professional systems analysts and award-winning teachers, these three authors capture the experience of actually developing and analyzing systems. They focus on the core set of skills that all analysts must possess––from gathering requirements and modeling business needs, to creating blueprints for how the system should be built.
by: Asuman G. Aksoy
publisher: Springer, published: 2009-12-17
sales rank: 751958
Education is an admirable thing, but it is well to remember from time to time that nothing worth knowing can be taught. Oscar Wilde, “The Critic as Artist,” 1890. Analysis is a profound subject; it is neither easy to understand nor summarize. However, Real Analysis can be discovered by solving problems. This book aims to give independent students the opportunity to discover Real Analysis by themselves through problem solving. ThedepthandcomplexityofthetheoryofAnalysiscanbeappreciatedbytakingaglimpseatits developmental history. Although Analysis was conceived in the 17th century during the Scienti?c Revolution, it has taken nearly two hundred years to establish its theoretical basis. Kepler, Galileo, Descartes, Fermat, Newton and Leibniz were among those who contributed to its genesis. Deep conceptual changes in Analysis were brought about in the 19th century by Cauchy and Weierstrass. Furthermore, modern concepts such as open and closed sets were introduced in the 1900s. Today nearly every undergraduate mathematics program requires at least one semester of Real Analysis. Often, students consider this course to be the most challenging or even intimidating of all their mathematics major requirements. The primary goal of this book is to alleviate those concerns by systematically solving the problems related to the core concepts of most analysis courses. In doing so, we hope that learning analysis becomes less taxing and thereby more satisfying.
publisher: IDEA GROUP PUBLISHING, published: 2010
sales rank: 962742
by: E. R. Kanasewich
publisher: The University of Alberta Press, published: 1974
sales rank: 3036763
Time sequence analysis is the study of relations between a sequence of data points or sequence of signals in order to determine the physical properties of the earth. Providing an up-to-date treatment on time series and time sequence, this book is intended for senior or graduate students in seismology, geomagnetism and exploratory geophysics.
by: Wes McKinney
publisher: O'Reilly Media, published: 2012-11-01
sales rank: 3477
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.
publisher: Cambridge University Press, published: 2009-04-20
sales rank: 105580
The Phylogenetic Handbook is a broad, hands on guide to theory and practice of nucleotide and protein phylogenetic analysis. This second edition includes six new chapters, covering topics such as Bayesian inference, tree topology testing and the impact of recombination on phylogenies, as well as a detailed section on molecular adaptation. The book has a stronger focus on hypothesis testing than the previous edition, with more extensive discussions on recombination analysis, detecting molecular adaptation and genealogy-based population genetics. Many chapters include elaborate practical sections, which have been updated to introduce the reader to the most recent versions of sequence analysis and phylogeny software, including BLAST, FastA, Clustal, T-coffee, Muscle, DAMBE, Tree-puzzle, Phylip, MEGA, PAUP*, IQPNNI, CONSEL, ModelTest, Prottest, PAML, HYPHY, MrBayes, BEAST, LAMARC, SplitsTree, and RDP. Many analysis tools are described by their original authors, resulting in clear explanations that constitute an ideal teaching guide for advanced-level undergraduate and graduate students.
publisher: Academic Press, published: 1987-11-28
by: Robert W. Brown
publisher: Wiley-Blackwell, published: 2014-06-23
sales rank: 326091
New edition explores contemporary MRI principles and practices
Thoroughly revised, updated and expanded, the second edition of Magnetic Resonance Imaging: Physical Principles and Sequence Design remains the preeminent text in its field. Using consistent nomenclature and mathematical notations throughout all the chapters, this new edition carefully explains the physical principles of magnetic resonance imaging design and implementation. In addition, detailed figures and MR images enable readers to better grasp core concepts, methods, and applications.
Magnetic Resonance Imaging, Second Edition begins with an introduction to fundamental principles, with coverage of magnetization, relaxation, quantum mechanics, signal detection and acquisition, Fourier imaging, image reconstruction, contrast, signal, and noise. The second part of the text explores MRI methods and applications, including fast imaging, water-fat separation, steady state gradient echo imaging, echo planar imaging, diffusion-weighted imaging, and induced magnetism. Lastly, the text discusses important hardware issues and parallel imaging.
Readers familiar with the first edition will find much new material, including:
Three appendices at the end of the text offer review material for basic electromagnetism and statistics as well as a list of acquisition parameters for the images in the book.
Acclaimed by both students and instructors, the second edition of Magnetic Resonance Imaging offers the most comprehensive and approachable introduction to the physics and the applications of magnetic resonance imaging.
Statistical Analysis of Next Generation Sequencing Data (Frontiers in Probability and the Statistical Sciences)
publisher: Springer, published: 2014-07-21
sales rank: 67221
Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine.
About the editors:
Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics.
Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.