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| analysis of biological data: The Analysis of Biological Data Michael C. Whitlock, Dolph Schluter, 2014-07-28 This second edition textbook teaches modern methods of statistics through the use of fascinating biological and medical case studies. The clear and engaging writing and practical perspective allows students to understand the analytical process behind biological data. Through the use of real world biological examples, biologists and health professionals can learn statistics in an essential manner. Authors Whitlock and Schulter have over 40 years’ experience between the two of them and therefore able to understand that students learn best through interesting examples and not overcomplicated formulas. This edition includes several unusual features that they have discovered to be helpful for effectively reaching their readers. |
| analysis of biological data: The Analysis of Biological Data Michael C. Whitlock, Dolph Schluter, 2018-01-17 Knowledge of statistics is essential in modern biology and medicine. Biologists and health professionals learn statistics best with real and interesting examples. The Analysis of Biological Data, Second Edition, by Whitlock and Schluter, teaches modern methods of statistics through the use of fascinating biological and medical cases. Readers consistently praise its clear and engaging writing and practical perspective. The second edition features over 200 new examples and problems. These include new calculation practice problems, which guide the student step by step through the methods, and a greater number of the examples and topics come from medical and human health research. Every chapter has been carefully edited for even greater clarity and ease of use. All the data sets, R scripts for all worked examples in the book, as well as many other teaching resources, are available to qualified instructors (see below). The Analysis of Biological Data is the most widely adopted introductory biological statistics textbook. It is now used at well over 200 schools and on every continent. |
| analysis of biological data: Analysis of Biological Data Sanghamitra Bandyopadhyay, 2007 Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter. Sample Chapter(s). Chapter 1: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (160 KB). Contents: Overview: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (H Tang & S Kim); An Introduction to Soft Computing (A Konar & S Das); Biological Sequence and Structure Analysis: Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound (J E Gallardo et al.); Classification of RNA Sequences with Support Vector Machines (J T L Wang & X Wu); Beyond String Algorithms: Protein Sequence Analysis Using Wavelet Transforms (A Krishnan & K-B Li); Filtering Protein Surface Motifs Using Negative Instances of Active Sites Candidates (N L Shrestha & T Ohkawa); Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction (G Pollastri et al.); In Silico Design of Ligands Using Properties of Target Active Sites (S Bandyopadhyay et al.); Gene Expression and Microarray Data Analysis: Inferring Regulations in a Genomic Network from Gene Expression Profiles (N Noman & H Iba); A Reliable Classification of Gene Clusters for Cancer Samples Using a Hybrid Multi-Objective Evolutionary Procedure (K Deb et al.); Feature Selection for Cancer Classification Using Ant Colony Optimization and Support Vector Machines (A Gupta et al.); Sophisticated Methods for Cancer Classification Using Microarray Data (S-B Cho & H-S Park); Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data (A Mukhopadhyay et al.). Readership: Graduate students and researchers in computer science, bioinformatics, computational and molecular biology, artificial intelligence, data mining, machine learning, electrical engineering, system science; researchers in pharmaceutical industries. |
| analysis of biological data: A Primer in Biological Data Analysis and Visualization Using R Gregg Hartvigsen, 2014-02-18 R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R. |
| analysis of biological data: Biological Data Analysis John C. Fry, 1993 Many biologists remain unfamiliar with statistical analysis and modelling, yet need to apply these techniques increasingly in their research. This volume describes how to analyze biological data, with commonly available software packages, without making errors which can invalidate results. Practical guidance is provided for planning the correct strategy for a variety of different statistical approaches and modelling problems and interpreting the results. Many examples of computer commands and output are given to illustrate the different analytical approaches. Biological Data Analysis: A Practical Approach has been designed specifically to allow researchers with only a minimal knowledge of statistics to understand a variety of statistical methods and apply them directly. The provision of data sets from several biological disciplines will make this book useful to all types of biologists. |
| analysis of biological data: Modern Analysis of Biological Data Stanislav Pekár, Marek Brabec, 2016-01-01 Kniha je zaměřena na regresní modely, konkrétně jednorozměrné zobecněné lineární modely (GLM). Je určena především studentům a kolegům z biologických oborů a vyžaduje pouze základní statistické vzdělání, jakým je např. jednosemestrový kurz biostatistiky. Text knihy obsahuje nezbytné minimum statistické teorie, především však řešení 18 reálných příkladů z oblasti biologie. Každý příklad je rozpracován od popisu a stanovení cíle přes vývoj statistického modelu až po závěr. K analýze dat je použit populární a volně dostupný statistický software R. Příklady byly záměrně vybrány tak, aby upozornily na leckteré problémy a chyby, které se mohou v průběhu analýzy dat vyskytnout. Zároveň mají čtenáře motivovat k tomu, jak o statistických modelech přemýšlet a jak je používat. Řešení příkladů si může čtenář vyzkoušet sám na datech, jež jsou dodávána spolu s knihou. |
| analysis of biological data: Data Processing Handbook for Complex Biological Data Sources Gauri Misra, 2019-03-23 Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. - Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level - Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data - Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing |
| analysis of biological data: Analysis of Biological Networks Björn H. Junker, Falk Schreiber, 2011-09-20 An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research. |
| analysis of biological data: The Analysis of Biological Data Michael C. Whitlock, Dolph Schluter, 2014-07-28 This second edition textbook teaches modern methods of statistics through the use of fascinating biological and medical case studies. The clear and engaging writing and practical perspective allows students to understand the analytical process behind biological data. Through the use of real world biological examples, biologists and health professionals can learn statistics in an essential manner. Authors Whitlock and Schulter have over 40 years’ experience between the two of them and therefore able to understand that students learn best through interesting examples and not overcomplicated formulas. This edition includes several unusual features that they have discovered to be helpful for effectively reaching their readers. |
| analysis of biological data: Biological Sequence Analysis Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, 1998-04-23 Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field. |
| analysis of biological data: Introduction to Computer-Intensive Methods of Data Analysis in Biology Derek A. Roff, 2006-05-25 Publisher Description |
| analysis of biological data: Biological Distance Analysis Marin A. Pilloud, Joseph T. Hefner, 2016-07-08 Biological Distance Analysis: Forensic and Bioarchaeological Perspectives synthesizes research within the realm of biological distance analysis, highlighting current work within the field and discussing future directions. The book is divided into three main sections. The first section clearly outlines datasets and methods within biological distance analysis, beginning with a brief history of the field and how it has progressed to its current state. The second section focuses on approaches using the individual within a forensic context, including ancestry estimation and case studies. The final section concentrates on population-based bioarchaeological approaches, providing key techniques and examples from archaeological samples. The volume also includes an appendix with additional resources available to those interested in biological distance analyses. - Defines datasets and how they are used within biodistance analysis - Applies methodology to individual and population studies - Bridges the sub-fields of forensic anthropology and bioarchaeology - Highlights current research and future directions of biological distance analysis - Identifies statistical programs and datasets for use in biodistance analysis - Contains cases studies and thorough index for those interested in biological distance analyses |
| analysis of biological data: Biological Knowledge Discovery Handbook Mourad Elloumi, Albert Y. Zomaya, 2013-12-24 The first comprehensive overview of preprocessing, mining,and postprocessing of biological data Molecular biology is undergoing exponential growth in both thevolume and complexity of biological data—and knowledgediscovery offers the capacity to automate complex search and dataanalysis tasks. This book presents a vast overview of the mostrecent developments on techniques and approaches in the field ofbiological knowledge discovery and data mining (KDD)—providingin-depth fundamental and technical field information on the mostimportant topics encountered. Written by top experts, Biological Knowledge DiscoveryHandbook: Preprocessing, Mining, and Postprocessing of BiologicalData covers the three main phases of knowledge discovery (datapreprocessing, data processing—also known as datamining—and data postprocessing) and analyzes both verificationsystems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological DataMining Combining sound theory with practical applications in molecularbiology, Biological Knowledge Discovery Handbook is idealfor courses in bioinformatics and biological KDD as well as forpractitioners and professional researchers in computer science,life science, and mathematics. |
| analysis of biological data: Topological Data Analysis for Genomics and Evolution Raúl Rabadán, Andrew J. Blumberg, 2019-10-31 Biology has entered the age of Big Data. The technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce questions to a comparison of algebraic invariants, such as numbers, which are typically easier to solve. Topological data analysis is a rapidly-developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology alongside mathematicians interested in applied topology. |
| analysis of biological data: Biological Data Mining in Protein Interaction Networks Li, Xiao-Li, Ng, See-Kiong, 2009-05-31 The goal of this book is to disseminate research results and best practices from cross-disciplinary researchers and practitioners interested in, and working on bioinformatics, data mining, and proteomics--Provided by publisher. |
| analysis of biological data: Statistical Design and Analysis of Biological Experiments Hans-Michael Kaltenbach, 2021-04-15 This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields. The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including ‘portable power’ formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice. Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software. Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable. |
| analysis of biological data: Analyzing Network Data in Biology and Medicine Nataša Pržulj, 2019-03-28 Introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, using real-world biological and medical examples. |
| analysis of biological data: Biostatistics with R Babak Shahbaba, 2011-12-15 Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text. |
| analysis of biological data: Computer Simulation and Data Analysis in Molecular Biology and Biophysics Victor Bloomfield, 2009-06-05 This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive. |
| analysis of biological data: Fitting Models to Biological Data Using Linear and Nonlinear Regression Harvey Motulsky, Arthur Christopoulos, 2004-05-27 Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists. |
| analysis of biological data: Managing Your Biological Data with Python Allegra Via, Kristian Rother, Anna Tramontano, 2014-03-18 Take Control of Your Data and Use Python with ConfidenceRequiring no prior programming experience, Managing Your Biological Data with Python empowers biologists and other life scientists to work with biological data on their own using the Python language. The book teaches them not only how to program but also how to manage their data. It shows how |
| analysis of biological data: Molecular Data Analysis Using R Csaba Ortutay, Zsuzsanna Ortutay, 2017-02-06 This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include: • Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered. • First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology. • Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users. |
| analysis of biological data: Biological Data Mining And Its Applications In Healthcare Xiaoli Li, See-kiong Ng, Jason T L Wang, 2013-11-28 Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. |
| analysis of biological data: Statistical Methods in Biology S.J. Welham, S.A. Gezan, S.J. Clark, A. Mead, 2014-08-22 Written in simple language with relevant examples, this illustrative introductory book presents best practices in experimental design and simple data analysis. Taking a practical and intuitive approach, it only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R. |
| analysis of biological data: Data-Centric Biology Sabina Leonelli, 2016-11-18 In recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life—including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities—as well as her own original empirical material—to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices. |
| analysis of biological data: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities Chakraborty, Shouvik, Mali, Kalyani, 2020-03-13 Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence. |
| analysis of biological data: Biological Data Mining Jake Y. Chen, Stefano Lonardi, 2009-09-01 Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin |
| analysis of biological data: Experimental Design and Data Analysis for Biologists Gerald Peter Quinn, Michael J. Keough, 2002-03-21 Regression, analysis of variance, correlation, graphical. |
| analysis of biological data: Analysis of Biological Development Klaus Kalthoff, 2001 This text, now available in full color, presents developmental biology as an ongoing process of enquiry, giving students a sense of the ways developmental biologists gain knowledge and a taste of the challenges ahead. The first part of the text focuses on the classical methods of analysis and the stages of embryonic development from gametogenesis to histogenesis. Part Two introduces the genetic and molecular analysis of development. The final part combines classical and modern types of analysis towards the investigation of long standing problems in development. Key experiments are described throughout to reinforce the relationship between scientific models and experimental data. |
| analysis of biological data: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
| analysis of biological data: S-Plus for the Analysis of Biological Data Rhondda E. Jones, Robin Gilliver, Simon Robson, Will Edwards, 2015-02-20 A manual to teach people to use the statistical software package S-Plus and to support the process of learning statistical concepts and methods. It is a useful workbook to accompany The Analysis of Biological Data by Whitlock and Schluter, published by Roberts and Co, Colorado. |
| analysis of biological data: Bioinformatics Data Skills Vince Buffalo, 2015-07 Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, youâ??ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand lifeâ??s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, youâ??re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles |
| analysis of biological data: Data Analysis in Biochemistry and Biophysics Magar Mager, 2012-12-02 Data Analysis in Biochemistry and Biophysics describes the techniques how to derive the most amount of quantitative and statistical information from data gathered in enzyme kinetics, protein-ligand equilibria, optical rotatory dispersion, chemical relaxation methods. This book focuses on the determination and analysis of parameters in different models that are used in biochemistry, biophysics, and molecular biology. The Michaelis-Menten equation can explain the process to obtain the maximum amount of information by determining the parameters of the model. This text also explains the fundamentals present in hypothesis testing, and the equation that represents the statistical aspects of a linear model occurring frequently in this field of testing. This book also analyzes the ultraviolet spectra of nucleic acids, particularly, to establish the composition of melting regions of nucleic acids. The investigator can use the matrix rank analysis to determine the spectra to substantiate systems whose functions are not known. This text also explains flow techniques and relaxation methods associated with rapid reactions to determine transient kinetic parameters. This book is suitable for molecular biologists, biophysicists, physiologists, biochemists, bio- mathematicians, statisticians, computer programmers, and investigators involved in related sciences |
| analysis of biological data: Philosophy of Science Timothy McGrew, Marc Alspector-Kelly, Fritz Allhoff, 2009-05-04 By combining excerpts from key historical writings with commentary by experts, Philosophy of Science: An Historical Anthology provides a comprehensive history of the philosophy of science from ancient to modern times. Provides a comprehensive history of the philosophy of science, from antiquity up to the 20th century Includes extensive commentary by scholars putting the selected writings in historical context and pointing out their interconnections Covers areas rarely seen in philosophy of science texts, including the philosophical dimensions of biology, chemistry, and geology Designed to be accessible to both undergraduates and graduate students |
| analysis of biological data: Quantitative Biology Brian Munsky, William S. Hlavacek, Lev S. Tsimring, 2018-08-21 An introduction to the quantitative modeling of biological processes, presenting modeling approaches, methodology, practical algorithms, software tools, and examples of current research. The quantitative modeling of biological processes promises to expand biological research from a science of observation and discovery to one of rigorous prediction and quantitative analysis. The rapidly growing field of quantitative biology seeks to use biology's emerging technological and computational capabilities to model biological processes. This textbook offers an introduction to the theory, methods, and tools of quantitative biology. The book first introduces the foundations of biological modeling, focusing on some of the most widely used formalisms. It then presents essential methodology for model-guided analyses of biological data, covering such methods as network reconstruction, uncertainty quantification, and experimental design; practical algorithms and software packages for modeling biological systems; and specific examples of current quantitative biology research and related specialized methods. Most chapters offer problems, progressing from simple to complex, that test the reader's mastery of such key techniques as deterministic and stochastic simulations and data analysis. Many chapters include snippets of code that can be used to recreate analyses and generate figures related to the text. Examples are presented in the three popular computing languages: Matlab, R, and Python. A variety of online resources supplement the the text. The editors are long-time organizers of the Annual q-bio Summer School, which was founded in 2007. Through the school, the editors have helped to train more than 400 visiting students in Los Alamos, NM, Santa Fe, NM, San Diego, CA, Albuquerque, NM, and Fort Collins, CO. This book is inspired by the school's curricula, and most of the contributors have participated in the school as students, lecturers, or both. Contributors John H. Abel, Roberto Bertolusso, Daniela Besozzi, Michael L. Blinov, Clive G. Bowsher, Fiona A. Chandra, Paolo Cazzaniga, Bryan C. Daniels, Bernie J. Daigle, Jr., Maciej Dobrzynski, Jonathan P. Doye, Brian Drawert, Sean Fancer, Gareth W. Fearnley, Dirk Fey, Zachary Fox, Ramon Grima, Andreas Hellander, Stefan Hellander, David Hofmann, Damian Hernandez, William S. Hlavacek, Jianjun Huang, Tomasz Jetka, Dongya Jia, Mohit Kumar Jolly, Boris N. Kholodenko, Markek Kimmel, Michał Komorowski, Ganhui Lan, Heeseob Lee, Herbert Levine, Leslie M Loew, Jason G. Lomnitz, Ard A. Louis, Grant Lythe, Carmen Molina-París, Ion I. Moraru, Andrew Mugler, Brian Munsky, Joe Natale, Ilya Nemenman, Karol Nienałtowski, Marco S. Nobile, Maria Nowicka, Sarah Olson, Alan S. Perelson, Linda R. Petzold, Sreenivasan Ponnambalam, Arya Pourzanjani, Ruy M. Ribeiro, William Raymond, William Raymond, Herbert M. Sauro, Michael A. Savageau, Abhyudai Singh, James C. Schaff, Boris M. Slepchenko, Thomas R. Sokolowski, Petr Šulc, Andrea Tangherloni, Pieter Rein ten Wolde, Philipp Thomas, Karen Tkach Tuzman, Lev S. Tsimring, Dan Vasilescu, Margaritis Voliotis, Lisa Weber |
| analysis of biological data: Continuum Analysis of Biological Systems G.K. Suraishkumar, 2014-07-08 This book addresses the analysis, in the continuum regime, of biological systems at various scales, from the cellular level to the industrial one. It presents both fundamental conservation principles (mass, charge, momentum and energy) and relevant fluxes resulting from appropriate driving forces, which are important for the analysis, design and operation of biological systems. It includes the concept of charge conservation, an important principle for biological systems that is not explicitly covered in any other book of this kind. The book is organized in five parts: mass conservation; charge conservation; momentum conservation; energy conservation and multiple conservations simultaneously applied. All mathematical aspects are presented step by step, allowing any reader with a basic mathematical background (calculus, differential equations, linear algebra, etc.) to follow the text with ease. The book promotes an intuitive understanding of all the relevant principles and in so doing facilitates their application to practical issues related to design and operation of biological systems. Intended as a self-contained textbook for students in biotechnology and in industrial, chemical and biomedical engineering, this book will also represent a useful reference guide for professionals working in the above-mentioned fields. |
| analysis of biological data: Large-Scale Data Streaming, Processing, and Blockchain Security Saini, Hemraj, Rathee, Geetanjali, Saini, Dinesh Kumar, 2020-08-14 Data has cemented itself as a building block of daily life. However, surrounding oneself with great quantities of information heightens risks to one’s personal privacy. Additionally, the presence of massive amounts of information prompts researchers into how best to handle and disseminate it. Research is necessary to understand how to cope with the current technological requirements. Large-Scale Data Streaming, Processing, and Blockchain Security is a collection of innovative research that explores the latest methodologies, modeling, and simulations for coping with the generation and management of large-scale data in both scientific and individual applications. Featuring coverage on a wide range of topics including security models, internet of things, and collaborative filtering, this book is ideally designed for entrepreneurs, security analysts, IT consultants, security professionals, programmers, computer technicians, data scientists, technology developers, engineers, researchers, academicians, and students. |
| analysis of biological data: Data Analysis in Molecular Biology and Evolution Xuhua Xia, 2007-05-08 Data Analysis in Molecular Biology and Evolution introduces biologists to DAMBE, a proprietary, user-friendly computer program for molecular data analysis. The unique combination of this book and software will allow biologists not only to understand the rationale behind a variety of computational tools in molecular biology and evolution, but also to gain instant access to these tools for use in their laboratories. Data Analysis in Molecular Biology and Evolution serves as an excellent resource for advanced level undergraduates or graduates as well as for professionals working in the field. |
| analysis of biological data: Computational Intelligence and Pattern Analysis in Biology Informatics Ujjwal Maulik, Sanghamitra Bandyopadhyay, Jason T. Wang, 2010-11-30 An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters. |
| analysis of biological data: Computing Skills for Biologists Stefano Allesina, Madlen Wilmes, 2019-01-15 A concise introduction to key computing skills for biologists While biological data continues to grow exponentially in size and quality, many of today’s biologists are not trained adequately in the computing skills necessary for leveraging this information deluge. In Computing Skills for Biologists, Stefano Allesina and Madlen Wilmes present a valuable toolbox for the effective analysis of biological data. Based on the authors’ experiences teaching scientific computing at the University of Chicago, this textbook emphasizes the automation of repetitive tasks and the construction of pipelines for data organization, analysis, visualization, and publication. Stressing practice rather than theory, the book’s examples and exercises are drawn from actual biological data and solve cogent problems spanning the entire breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. Beginners will benefit from the many examples explained step-by-step, while more seasoned researchers will learn how to combine tools to make biological data analysis robust and reproducible. The book uses free software and code that can be run on any platform. Computing Skills for Biologists is ideal for scientists wanting to improve their technical skills and instructors looking to teach the main computing tools essential for biology research in the twenty-first century. Excellent resource for acquiring comprehensive computing skills Both novice and experienced scientists will increase efficiency by building automated and reproducible pipelines for biological data analysis Code examples based on published data spanning the breadth of biological disciplines Detailed solutions provided for exercises in each chapter Extensive companion website |
analysis 与 analyses 有什么区别? - 知乎
也就是说，当analysis 在具体语境中表示抽象概念时，它就成为了不可数名词，本身就没有analyses这个复数形式，二者怎么能互换呢？ 当analysis 在具体语境中表示可数名词概念时（ …
Geopolitics: Geopolitical news, analysis, & discussion - Reddit
Geopolitics is focused on the relationship between politics and territory. Through geopolitics we attempt to analyze and predict the actions and decisions of nations, or other forms of political …
r/StockMarket - Reddit's Front Page of the Stock Market
Welcome to /r/StockMarket! Our objective is to provide short and mid term trade ideas, market analysis & commentary for active traders and investors. Posts about equities, options, forex, …
Alternate Recipes In-Depth Analysis - An Objective Follow-up
Sep 14, 2021 · This analysis in the spreadsheet is completely objective. The post illustrates only one of the many playing styles, the criteria of which are clearly defined in the post - a middle of …
What is the limit for number of files and data analysis for ... - Reddit
Jun 19, 2024 · Number of Files: You can upload up to 25 files concurrently for analysis. This includes a mix of different types, such as documents, images, and spreadsheets. Data …
为什么很多人认为TPAMI是人工智能所有领域的顶刊？ - 知乎
Dec 15, 2024 · TPAMI全称是IEEE Transactions on Pattern Analysis and Machine Intelligence，从名字就能看出来，它关注的是"模式分析"和"机器智能"这两个大方向。这两个 …
The UFO reddit
Aug 31, 2022 · We have declassified documents about anomalous incidents that directly conflict the new AARO report to a point it makes me wonder what they are even doing.
origin怎么进行线性拟合 求步骤和过程？ - 知乎
在 Graph 1 为当前激活窗口时，点击 Origin 菜单栏上的 Analysis ——> Fitting ——> Linear Fit ——> Open Dialog。直接点 OK 就可以了。 完成之后，你会在 Graph 1 中看到一条红色的直线 …
X射线光电子能谱（XPS）
X射线光电子能谱（XPS）是一种用于分析材料表面化学成分和电子状态的先进技术。
Do AI-Based Trading Bots Actually Work for Consistent Profit?
Sep 18, 2023 · Statisitical analysis of human trends in sentiment seems to be a reasonable approach to anticipating changes in sentiment which drives some amount of trading behaviors. …
analysis 与 analyses 有什么区别? - 知乎
也就是说，当analysis 在具体语境中表示抽象概念时，它就成为了不可数名词，本身就没有analyses这个复数形式，二者怎么能互换呢？ 当analysis 在具体语境中表示可数名词概念时（ …
Geopolitics: Geopolitical news, analysis, & discussion - Reddit
Geopolitics is focused on the relationship between politics and territory. Through geopolitics we attempt to analyze and predict the actions and decisions of nations, or other forms of political …
r/StockMarket - Reddit's Front Page of the Stock Market
Welcome to /r/StockMarket! Our objective is to provide short and mid term trade ideas, market analysis & commentary for active traders and investors. Posts about equities, options, forex, …
Alternate Recipes In-Depth Analysis - An Objective Follow-up
Sep 14, 2021 · This analysis in the spreadsheet is completely objective. The post illustrates only one of the many playing styles, the criteria of which are clearly defined in the post - a middle of …
What is the limit for number of files and data analysis for ... - Reddit
Jun 19, 2024 · Number of Files: You can upload up to 25 files concurrently for analysis. This includes a mix of different types, such as documents, images, and spreadsheets. Data …
为什么很多人认为TPAMI是人工智能所有领域的顶刊？ - 知乎
Dec 15, 2024 · TPAMI全称是IEEE Transactions on Pattern Analysis and Machine Intelligence，从名字就能看出来，它关注的是"模式分析"和"机器智能"这两个大方向。这两个方向恰恰是人工 …
The UFO reddit
Aug 31, 2022 · We have declassified documents about anomalous incidents that directly conflict the new AARO report to a point it makes me wonder what they are even doing.
origin怎么进行线性拟合 求步骤和过程？ - 知乎
在 Graph 1 为当前激活窗口时，点击 Origin 菜单栏上的 Analysis ——> Fitting ——> Linear Fit ——> Open Dialog。直接点 OK 就可以了。 完成之后，你会在 Graph 1 中看到一条红色的直线 …
X射线光电子能谱（XPS）
X射线光电子能谱（XPS）是一种用于分析材料表面化学成分和电子状态的先进技术。
Do AI-Based Trading Bots Actually Work for Consistent Profit?
Sep 18, 2023 · Statisitical analysis of human trends in sentiment seems to be a reasonable approach to anticipating changes in sentiment which drives some amount of trading behaviors. …
