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Genome Data Analysis (Learning Materials in Biosciences)
by Ju Han KimThis textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing
by Veli Mäkinen Djamal Belazzougui Fabio Cunial Alexandru I. TomescuHigh-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows-Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.
Genomic Approaches for Cross-Species Extrapolation in Toxicology
by William H. Benson Richard T. Di GiulioThe latest tools for investigating stress response in organisms, genomic technologies provide great insight into how different organisms respond to environmental conditions. However, their usefulness needs to be tested, verified, and codified. Genomic Approaches for Cross-Species Extrapolation in Toxicology provides a balanced discussion drawn from
Genomics Data Analysis for Crop Improvement (Springer Protocols Handbooks)
by Girish Chandra Kuldeep Kumar Priyanka Anjoy Kishor GaikwadThis book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.
Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods
by David R. BickelStatisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.Key Features:* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data* gradual introduction to the mathematical equations needed* how to choose between different methods of multiple hypothesis testing* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates* guidance through the minefield of current criticisms of p values* material on non-Bayesian prior p values and posterior p values not previously published
Genomics and Bioinformatics
by Tore SamuelssonWith the arrival of genomics and genome sequencing projects, biology has been transformed into an incredibly data-rich science. The vast amount of information generated has made computational analysis critical and has increased demand for skilled bioinformaticians. Designed for biologists without previous programming experience, this textbook provides a hands-on introduction to Unix, Perl and other tools used in sequence bioinformatics. Relevant biological topics are used throughout the book and are combined with practical bioinformatics examples, leading students through the process from biological problem to computational solution. All of the Perl scripts, sequence and database files used in the book are available for download at the accompanying website, allowing the reader to easily follow each example using their own computer. Programming examples are kept at an introductory level, avoiding complex mathematics that students often find daunting. The book demonstrates that even simple programs can provide powerful solutions to many complex bioinformatics problems.
Genomics in the Cloud: Using Docker, GATK, and WDL in Terra
by Geraldine A. Van der Auwera Brian D. O'ConnorData in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytesâ??or over 50 million gigabytesâ??of genomic data, and theyâ??re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud?With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian Oâ??Connor of the UC Santa Cruz Genomics Institute, guide you through the process. Youâ??ll learn by working with real data and genomics algorithms from the field.This book covers:Essential genomics and computing technology backgroundBasic cloud computing operationsGetting started with GATK, plus three major GATK Best Practices pipelinesAutomating analysis with scripted workflows using WDL and CromwellScaling up workflow execution in the cloud, including parallelization and cost optimizationInteractive analysis in the cloud using Jupyter notebooksSecure collaboration and computational reproducibility using Terra
Gentrification Trends in the United States
by Richard W. MartinGentrification Trends in the United States is the first book to quantify the changes that take place when a neighborhood’s income level, educational attainment, or occupational makeup outpace the city as a whole – the much-debated yet poorly understood phenomenon of gentrification. Applying a novel method to four decades of U.S. Census data, this resource for students and scholars provides a quantitative basis for the nuanced demographic trends uncovered through ethnography and other forms of qualitative research. This analysis of a rich data source characterized by a broad regional and chronological scope provides new insight into larger questions about the nature and prevalence of gentrification across the United States. Has gentrification become more common over time? Which cities have experienced the most gentrification? Is gentrification widespread, or does it tend to be concentrated in a small number of cities? Has the nature of gentrification changed over time? Ideal reading for courses in real estate, urban planning, urban economics, sociology, geography, econometrics, and GIS, this pathbreaking addition to the urban studies literature will enrich the perspective of any scholar of U.S. cities.
Gentzen Calculi for Modal Propositional Logic
by Francesca PoggiolesiThe book is about Gentzen calculi for (the main systems of) modal logic. It is divided into three parts. In the first part we introduce and discuss the main philosophical ideas related to proof theory, and we try to identify criteria for distinguishing good sequent calculi. In the second part we present the several attempts made from the 50's until today to provide modal logic with Gentzen calculi. In the third and and final part we analyse new calculi for modal logics, called tree-hypersequent calculi, which were recently introduced by the author. We show in a precise and clear way the main results that can be proved with and about them.
Gentzen's Centenary
by Reinhard Kahle Michael RathjenGerhard Gentzen has been described as logic's lost genius, whom Gödel called a better logician than himself. This work comprises articles by leading proof theorists, attesting to Gentzen's enduring legacy to mathematical logic and beyond. The contributions range from philosophical reflections and re-evaluations of Gentzen's original consistency proofs to the most recent developments in proof theory. Gentzen founded modern proof theory. His sequent calculus and natural deduction system beautifully explain the deep symmetries of logic. They underlie modern developments in computer science such as automated theorem proving and type theory.
Geo-Environmental Hazards using AI-enabled Geospatial Techniques and Earth Observation Systems (Advances in Geographic Information Science)
by Jung-Sup Um Tanupriya Choudhury Bappaditya Koley Anindita Nath Atul Kumar PatidarThis edited collection provides a comprehensive exploration of cutting-edge ideas, approaches, simulations, evaluations of risk, and systems that enhance the practicality of current geospatial technologies for reducing hazard risks. The various sections within this book delve into subjects such as the foundational principles of Earth Observation Systems (EOS) and geospatial methodologies. Additionally, the text serves as an advisory resource on the collaborative use of satellite-derived data and artificial intelligence to track and alleviate geo-environmental threats. The volume imparts extensive understanding regarding geo-environmental dangers and their analysis via EOS along with geospatial strategies. It encompasses key hazard-related themes including coastal degradation, predisposition to landslides, mapping vegetation coverages, tropical storm patterns, soil depletion due to erosion processes, vulnerability to rapid or extended flooding events, variations in oceansurface temperatures alongside chlorophyll-a levels; it also addresses assessments related to groundwater reserves and quality measures as well as sustainable management practices for watersheds that support community livelihoods—all through leveraging AI-integrated geospatial tools in conjunction with earth observation technologies. Furthermore, this work engages in discourse about systems designed for mitigating these ecological challenges sustainably. Scholars engaged in research activities; educational professionals; those involved in landscape design; engineers working at ground level; individuals responsible for policy-making—all who are concerned with geo-environmental hazards or associated domains—will find valuable insights within these pages.
Geo-intelligence for Sustainable Development (Advances in Geographical and Environmental Sciences)
by R. B. Singh T. P. Singh Dharmaveer SinghGlobally, concerns for the environment and human well-being have increased as results of threats imposed by climate change and disasters, environmental degradation, pollution of natural resources, water scarcity and proliferation of slums. Finding appropriate solutions to these threats and challenges is not simple, as these are generally complex and require state-of-the-art technology to collect, measure, handle and analyse large volumes of varying data sets. However, the recent advances in sensor technology, coupled with the rapid development of computational power, have greatly enhanced our abilities to capture, store and analyse the surrounding physical environment. This book explores diverse dimensions of geo-intelligence (GI) technology in developing a computing framework for location-based, data-integrating earth observation and predictive modelling to address these issues at all levels and scales. The book provides insight into the applications of GI technology in several fields of spatial and social sciences and attempts to bridge the gap between them.
GeoComputation
by Robert J. Abrahart Linda SeeA revision of Openshaw and Abrahart's seminal work, GeoComputation, Second Edition retains influences of its originators while also providing updated, state-of-the-art information on changes in the computational environment. In keeping with the field's development, this new edition takes a broader view and provides comprehensive coverage across the
Geochemical Mechanics and Deep Neural Network Modeling: Applications to Earthquake Prediction (Advances in Geological Science)
by Mitsuhiro ToriumiThe recent understandings about global earth mechanics are widely based on huge amounts of monitoring data accumulated using global networks of precise seismic stations, satellite monitoring of gravity, very large baseline interferometry, and the Global Positioning System. New discoveries in materials sciences of rocks and minerals and of rock deformation with fluid water in the earth also provide essential information. This book presents recent work on natural geometry, spatial and temporal distribution patterns of various cracks sealed by minerals, and time scales of their crack sealing in the plate boundary. Furthermore, the book includes a challenging investigation of stochastic earthquake prediction testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the generative autoencoder modeling of global correlated seismicity. Their manifestation in this book contributes to the development of human society resilient from natural hazards. Presented here are (1) mechanics of natural crack sealing and fluid flow in the plate boundary regions, (2) large-scale permeable convection of the plate boundary, (3) the rapid process of massive extrusion of plate boundary rocks, (4) synchronous satellite gravity and global correlated seismicity, (5) Gaussian network dynamics of global correlated seismicity, and (6) prediction testing of plate boundary earthquakes by machine learning and generative autoencoders.
Geocoding Health Data: The Use of Geographic Codes in Cancer Prevention and Control, Research and Practice
by Dale L. Zimmerman Gerard Rushton Marc P. Armstrong Josephine Gittler Barry R. Greene Claire E. Pavlik Michele M. WestIn the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of ge
Geocomputation with Python (Chapman & Hall/CRC The Python Series)
by Michael Dorman Anita Graser Jakub Nowosad Robin LovelaceGeocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.Key features: Showcases the integration of vector and raster datasets operations. Provides explanation of each line of code in the book to minimize surprises. Includes example datasets and meaningful operations to illustrate the applied nature of geographic research. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community.The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for data science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, machine learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.
Geocomputation with R (Chapman & Hall/CRC The R Series)
by Robin Lovelace Jakub Nowosad Jannes MuenchowGeocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.
Geocomputation with R (Chapman & Hall/CRC The R Series)
by Robin Lovelace Jakub Nowosad Jannes MuenchowGeocomputation with R is for people who want to analyze, visualize, and model geographic data with open source software. The book provides a foundation for learning how to solve a wide range of geographic data analysis problems in a reproducible, and therefore scientifically sound and scalable way. The second edition features numerous updates, including the adoption of the high-performance terra package for all raster data processing, detailed coverage of the spherical geometry engine s2, updated information on coordinate reference systems and new content on openEO, STAC, COG, and gdalcubes. The data visualization chapter has been revamped around version 4 of the tmap package, providing a fresh perspective on creating publication-quality maps from the command line. The importance of the book is also highlighted in a new foreword by Edzer Pebesma.The book equips you with the knowledge and skills necessary to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. The book is especially well-suited to: Data scientists and engineers interested in upskilling to handle spatial data. People with existing geographic data skills interested in developing powerful geosolutions via code. Anyone who needs to work with spatial data in a reproducible and scalable way. The book is divided into three parts: Foundations, Extensions, and Applications, covering progressively more advanced topics. The exercises at the end of each chapter provide the necessary skills to address various geospatial problems, with solutions and supplementary materials available at r.geocompx.org/solutions/.
Geodesic Convexity in Graphs
by Ignacio M. PelayoGeodesic Convexity in Graphs is devoted to the study of the geodesic convexity on finite, simple, connected graphs. The first chapter includes the main definitions and results on graph theory, metric graph theory and graph path convexities. The following chapters focus exclusively on the geodesic convexity, including motivation and background, specific definitions, discussion and examples, results, proofs, exercises and open problems. The main and most studied parameters involving geodesic convexity in graphs are both the geodetic and the hull number which are defined as the cardinality of minimum geodetic and hull set, respectively. This text reviews various results, obtained during the last one and a half decade, relating these two invariants and some others such as convexity number, Steiner number, geodetic iteration number, Helly number, and Caratheodory number to a wide range a contexts, including products, boundary-type vertex sets, and perfect graph families. This monograph can serve as a supplement to a half-semester graduate course in geodesic convexity but is primarily a guide for postgraduates and researchers interested in topics related to metric graph theory and graph convexity theory.
Geodetic Boundary Value Problem: the Equivalence between Molodensky’s and Helmert’s Solutions
by Fernando Sansò Michael G. SiderisThis book offers a new approach to interpreting the geodetic boundary value problem, successfully obtaining the solutions of the Molodensky and Stokes boundary value problems (BVPs) with the help of downward continuation (DC) based methods. Although DC is known to be an improperly posed operation, classical methods seem to provide numerically sensible results, and therefore it can be concluded that such classical methods must in fact be manifestations of different, mathematically sound approaches. Here, the authors first prove the equivalence of Molodensky's and Stoke's approaches with Helmert's reduction in terms of both BVP formulation and BVP solutions by means of the DC method. They then go on to show that this is not merely a downward continuation operation, and provide more rigorous interpretations of the DC approach as a change of boundary approach and as a pseudo BVP solution approach.
Geodetic Time Series Analysis in Earth Sciences (Springer Geophysics)
by Jean-Philippe Montillet Machiel S. BosThis book provides an essential appraisal of the recent advances in technologies, mathematical models and computational software used by those working with geodetic data. It explains the latest methods in processing and analyzing geodetic time series data from various space missions (i.e. GNSS, GRACE) and other technologies (i.e. tide gauges), using the most recent mathematical models. The book provides practical examples of how to apply these models to estimate seal level rise as well as rapid and evolving land motion changes due to gravity (ice sheet loss) and earthquakes respectively. It also provides a necessary overview of geodetic software and where to obtain them.
Geographic Data Analysis Using R
by Xindong HeThis book is structured to encompass both the foundational and specialized aspects of quantitative analysis in geography. The basic content covers descriptive statistical analysis and correlation analysis of geographical data, while the professional content delves into more advanced topics like linear regression analysis, geographically weighted regression analysis, time series analysis, cluster analysis, principal component analysis, Markov chain analysis, and geographical network analysis. The methodologies span from widely utilized techniques to more recent developments, and the data primarily originates from reputable sources in China. The example code provided in the book can be executed using R packages available on the CRAN website.This book is an invaluable resource for undergraduate and graduate students, as well as researchers interested in learning and applying R for processing, visualizing, and analyzing geographic data. It serves as an introductory course in quantitative methods in geography for students in geography departments. Additionally, it is an ideal supplementary text for applied methods courses across various disciplines that involve geographic data, such as human and physical geography, geographic information science, ecology, public health, crime, and economics.
Geographic Data Science with R: Visualizing and Analyzing Environmental Change (Chapman & Hall/CRC Data Science Series)
by Michael C. WimberlyThe burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets, including descriptive, explanatory, and predictive analytics. However, applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values, handling missing data, harmonizing data from multiple sources, summarizing the data, and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods, few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment, which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular, vector, and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R, it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R. Key Features: Focus on developing practical workflows for processing and integrating multiple sources of geospatial data in R Example-based approach that teaches R programming and data science concepts through real-world applications related to climate, land cover and land use, and natural hazards. Consistent use of tidyverse packages for tabular data manipulation and visualization. Strong focus on analysing continuous and categorical raster datasets using the new terra package Organized so that each chapter builds on the topics and techniques covered in the preceding chapters Can be used for self-study or as the textbook for a geospatial science course.
Geographic Information, Geospatial Technologies and Spatial Data Science for Health
by Justine BlanfordGeographic information, spatial analysis and geospatial technologies play an important role in understanding changes in planetary health and in defining the drivers contributing to different health outcomes both locally and globally. Patterns influencing health outcomes and disease in the environment are complex and require an understanding of the ecology of the disease and how these interact in space and time. Knowing where and when diseases are prevalent, who is affected and what may be driving these outcomes is important for determining how to respond. In reality, we all would like to be healthy and live in healthy places.In this book, epidemiology and public health are integrated with spatial data science to examine health issues in dynamically changing environments. This is too broad a field to be completely covered in one book, and so, it has been necessary to be selective with the topics, methods and examples used to avoid overwhelming introductory readers while at the same time providing sufficient depth for geospatial experts interested in health and for health professionals interested in integrating geospatial elements for health analysis. A variety of geographic information (some novel, some volunteered, some authoritative, some big and messy) is used with a mix of methods consisting of spatial analysis, data science and spatial statistics to better understand health risks and disease outcomes.Key Features: Makes spatial data science accessible to health Integrates epidemiology and disease ecology with spatial data science Integrates theoretical geographic information science concepts Provides practical and applied approaches for examining and exploring health and disease risks Provides spatial data science skill development ranging from map making to spatial modelling
Geographical Modeling: Cities and Territories
by Denise PumainThe modeling of cities and territories has progressed greatly in the last 20 years. This is firstly due to geographic information systems, followed by the availability of large amounts of georeferenced data – both on the Internet and through the use of connected objects. In addition, the rise in performance of computational methods for the simulation and exploration of dynamic models has facilitated advancement. Geographical Modeling presents previously unpublished information on the main advances achieved by these new approaches. Each of the six chapters builds a bibliographic review and precisely describes the methods used, highlighting their advantages and discussing their interpretations. They are all illustrated by many examples. The book also explains with clarity the theoretical foundations of geographical analysis, the delicate operations of model selection, and the applications of fractals and scaling laws. These applications include gaining knowledge of the morphology of cities and the organization of urban transport, and finding new methods of building and exploring simulation models and visualizations of data and results.