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| inference biology: Learning and Inference in Computational Systems Biology Neil D. Lawrence, 2010 Tools and techniques for biological inference problems at scales ranging from genome-wide to pathway-specific. Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model--in other words, to answer specific questions about the underlying mechanisms of a biological system--in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks.The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built. Florence d'Alch e-Buc, John Angus, Matthew J. Beal, Nicholas Brunel, Ben Calderhead, Pei Gao, Mark Girolami, Andrew Golightly, Dirk Husmeier, Johannes Jaeger, Neil D. Lawrence, Juan Li, Kuang Lin, Pedro Mendes, Nicholas A. M. Monk, Eric Mjolsness, Manfred Opper, Claudia Rangel, Magnus Rattray, Andreas Ruttor, Guido Sanguinetti, Michalis Titsias, Vladislav Vyshemirsky, David L. Wild, Darren Wilkinson, Guy Yosiphon |
| inference biology: Network Inference in Molecular Biology Jesse M. Lingeman, Dennis Shasha, 2012-05-24 Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable. |
| inference biology: Likelihood and Bayesian Inference Leonhard Held, Daniel Sabanés Bové, 2020-03-31 This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book “Applied Statistical Inference” has been expanded to include new material on Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis, and each chapter is complemented by exercises. The text is primarily intended for graduate statistics and biostatistics students with an interest in applications. |
| inference biology: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| inference biology: Species Tree Inference Laura Kubatko, L. Lacey Knowles, 2023-03-14 An up-to-date reference book on phylogenetic methods and applications for evolutionary biologists The increasingly widespread availability of genomic data is transforming how biologists estimate evolutionary relationships among organisms and broadening the range of questions that researchers can test in a phylogenetic framework. Species Tree Inference brings together many of today’s leading scholars in the field to provide an incisive guide to the latest practices for analyzing multilocus sequence data. This wide-ranging and authoritative book gives detailed explanations of emerging new approaches and assesses their strengths and challenges, offering an invaluable context for gauging which procedure to apply given the types of genomic data and processes that contribute to differences in the patterns of inheritance across loci. It demonstrates how to apply these approaches using empirical studies that span a range of taxa, timeframes of diversification, and processes that cause the evolutionary history of genes across genomes to differ. By fully embracing this genomic heterogeneity, Species Tree Inference illustrates how to address questions beyond the goal of estimating phylogenetic relationships of organisms, enabling students and researchers to pursue their own research in statistically sophisticated ways while charting new directions of scientific discovery. |
| inference biology: Active Inference Thomas Parr, Giovanni Pezzulo, Karl J. Friston, 2022-03-29 The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning. |
| inference biology: Simultaneous Statistical Inference Thorsten Dickhaus, 2014-01-23 This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students. |
| inference biology: Gene Network Inference Alberto Fuente, 2014-01-03 This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians. |
| inference biology: Handbook on Biological Networks Stefano Boccaletti, 2010 Networked systems are all around us. The accumulated evidence of systems as complex as a cell cannot be fully understood by studying only their isolated constituents, giving rise to a new area of interest in research OCo the study of complex networks . In a broad sense, biological networks have been one of the most studied networks, and the field has benefited from many important contributions. By understanding and modeling the structure of a biological network, a better perception of its dynamical and functional behavior is to be expected. This unique book compiles the most relevant results and novel insights provided by network theory in the biological sciences, ranging from the structure and dynamics of the brain to cellular and protein networks and to population-level biology. Sample Chapter(s). Chapter 1: Introduction (61 KB). Contents: Networks at the Cellular Level: The Structural Network Properties of Biological Systems (M Brilli & P Li); Dynamics of Multicellular Synthetic Gene Networks (E Ullner et al.); Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level (J Thakar & R Albert); Complexity of Boolean Dynamics in Simple Models of Signaling Networks and in Real Genetic Networks (A D az-Guilera & R ulvarez-Buylla); Geometry and Topology of Folding Landscapes (L Bongini & L Casetti); Elastic Network Models for Biomolecular Dynamics: Theory and Application to Membrane Proteins and Viruses (T R Lezon et al.); Metabolic Networks (M C Palumbo et al.); Brain Networks: The Human Brain Network (O Sporns); Brain Network Analysis from High-Resolution EEG Signals (F De Vico Fallani & F Babiloni); An Optimization Approach to the Structure of the Neuronal layout of C elegans (A Arenas et al.); Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory (N Raichman et al.); Synchrony and Precise Timing in Complex Neural Networks (R-M Memmesheimer & M Timme); Networks at the Individual and Population Levels: Ideas for Moving Beyond Structure to Dynamics of Ecological Networks (D B Stouffer et al.); Evolutionary Models for Simple Biosystems (F Bagnoli); Evolution of Cooperation in Adaptive Social Networks (S Van Segbroeck et al.); From Animal Collectives and Complex Networks to Decentralized Motion Control Strategies (A Buscarino et al.); Interplay of Network State and Topology in Epidemic Dynamics (T Gross). Readership: Advanced undergraduates, graduate students and researchers interested in the study of complex networks in a wide range of biological processes and systems. |
| inference biology: A Companion to the Philosophy of History and Historiography Aviezer Tucker, 2011-06-28 A COMPANION TO THE PHILOSOPHY OF HISTORY AND HISTORIOGRAPHY The philosophy of historiography examines our representations and knowledge of the past, the relation between evidence, inference, explanation and narrative. Do we possess knowledge of the past? Do we just have probable beliefs about the past, or is historiography a piece of convincing fiction? The philosophy of history is the direct philosophical examination of history, whether it is necessary or contingent, whether it has a direction or whether it is coincidental, and if it has a direction, what it is, and how and why it is unfolding? The fifty entries in this Companion cover the main issues in the philosophies of historiography and history, including natural history and the practices of historians. Written by an international and multi-disciplinary group of experts, these clearly written entries present a cutting-edge updated picture of current research in the philosophies of historiography and history. This Companion will be of interest to philosophers, historians, natural historians, and social scientists. |
| inference biology: Quantitative Analyses in Wildlife Science Leonard A. Brennan, Andrew N. Tri, Bruce G. Marcot, 2019-09-10 An authoritative guide to quantitative methods that will help wildlife scientists improve analysis and decision-making. Over the past fifty years, wildlife science has become increasingly quantitative. But to wildlife scientists, many of whom have not been formally trained as biometricians, computer modelers, or mathematicians, the wide array of available techniques for analyzing wildlife populations and habitats can be overwhelming. This practical book aims to help students and professionals alike understand how to use quantitative methods to inform their work in the field. Covering the most widely used contemporary approaches to the analysis of wildlife populations and habitats, Quantitative Analyses in Wildlife Science is divided into five broad areas: • general statistical methods • demographic estimation • dynamic process modeling • analysis of spatially based data on animals and resources • numerical methods Addressing a variety of topics, from population estimation and growth trend predictions to the study of migration patterns, this book presents fresh data on such pressing issues as sustainable take, control of invasives, and species reintroduction. Authored by leading researchers in wildlife science, each chapter considers the structure of data in relation to a particular analytical technique, as well as the structure of variation in those data. Providing conceptual and quantitative overviews of modern analytical methods, the techniques covered in this book also apply to conservation research and wildlife policy. Whether a quick refresher or a comprehensive introduction is called for, Quantitative Analyses in Wildlife Science is an indispensable addition to every wildlife professional's bookshelf. Contributors: William M. Block, Leonard A. Brennan, Stephen T. Buckland, Christopher C. Chizinski, Evan C. Cooch, Raymond J. Davis, Stephen J. DeMaso, Randy W. DeYoung, Jane Elith, Joseph J. Fontane, Julie A. Heinrichs, Mevin B. Hooten, Julianna M. A. Jenkins, Zachary S. Laden, Damon B. Lesmeister, Daniel Linden, Jeffrey J. Lusk, Bruce G. Marcot, David L. Miller, Michael L. Morrison, Eric Rexstad, Jamie S. Sanderlin, Joseph P. Sands, Erica F. Stuber, Chris Sutherland, Andrew N. Tri, David B. Wester, Gary C. White, Christopher K. Williams, Damon L. Williford |
| inference biology: Phylogenetic Supertrees O. R. P. Bininda-Emonds, 2004-08-25 This is the first book on phylogenetic supertrees, a recent, but controversial development for inferring evolutionary trees. Rather than analyze the combined primary character data directly, supertree construction proceeds by combining the tree topologies derived from those data. This difference in strategy has allowed for the exciting possibility of larger, more complete phylogenies than are otherwise currently possible, with the potential to revolutionize evolutionarily-based research. This book provides a comprehensive look at supertrees, ranging from the methods used to build supertrees to the significance of supertrees to bioinformatic and biological research. Reviews of many the major supertree methods are provided and four new techniques, including a Bayesian implementation of supertrees, are described for the first time. The far-reaching impact of supertrees on biological research is highlighted both in general terms and through specific examples from diverse clades such as flowering plants, even-toed ungulates, and primates. The book also critically examines the many outstanding challenges and problem areas for this relatively new field, showing the way for supertree construction in the age of genomics. Interdisciplinary contributions from the majority of the leading authorities on supertree construction in all areas of the bioinformatic community (biology, computer sciences, and mathematics) will ensure that this book is a valuable reference with wide appeal to anyone interested in phylogenetic inference. |
| inference biology: Handbook of Statistical Genomics David J. Balding, Ida Moltke, John Marioni, 2019-09-10 A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. |
| inference biology: Biological and Cultural Bases of Human Inference Riccardo Viale, Daniel Andler, Lawrence A. Hirschfeld, 2013-05-13 Biological and Cultural Bases of Human Inference addresses the interface between social science and cognitive science. In this volume, Viale and colleagues explore which human social cognitive powers evolve naturally and which are influenced by culture. Updating the debate between innatism and culturalism regarding human cognitive abilities, this book represents a much-needed articulation of these diverse bases of cognition. Chapters throughout the book provide social science and philosophical reflections, in addition to the perspective of evolutionary theory and the central assumptions of cognitive science. The overall approach of the text is based on three complementary levels: adult performance, cognitive development, and cultural history and prehistory. Scholars from several disciplines contribute to this volume, including researchers in cognitive, developmental, social and evolutionary psychology, neuropsychology, cognitive anthropology, epistemology, and philosophy of mind. This contemporary, important collection appeals to researchers in the fields of cognitive, social, developmental, and evolutionary psychology and will prove valuable to researchers in the decision sciences. |
| inference biology: Animal Communication Theory Ulrich Stegmann, 2013-05-02 A valuable overview and analysis of foundational concepts in animal behaviour studies, including information, meaning, communication, signals and cues. Its comprehensive introduction and numerous illustrations will make it accessible to students and researchers from a wide variety of academic backgrounds, ranging from ethology and evolutionary biology to philosophy of mind. |
| inference biology: The Utility of Strategic Surveys for Rare and Little-known Species Under the Northwest Forest Plan Deanna H. Olson, 2007 |
| inference biology: Piaget's Conception of Evolution John Gerard Messerly, 1996 The first full-length study of Jean Piaget as a philosopher and evolutionist. Messerly traces Piaget's earliest conjectures about knowledge through its further developments to its mature formulation as 'genetic epistemology.' Messerly analyzes Piaget's constructivist theory of the evolution of human knowledge as continuous with, yet partially transcending, the biological process of adaptation to the environment. Messerly's study serves as an invitation to further explorations with Paiget's theory and will interest philosophers, biologists, and psychologists. |
| inference biology: Network Bioscience, 2nd Edition Marco Pellegrini, Marco Antoniotti, Bud Mishra, 2020-03-27 Network science has accelerated a deep and successful trend in research that influences a range of disciplines like mathematics, graph theory, physics, statistics, data science and computer science (just to name a few) and adapts the relevant techniques and insights to address relevant but disparate social, biological, technological questions. We are now in an era of 'big biological data' supported by cost-effective high-throughput genomic, transcriptomic, proteomic, metabolomic data collection techniques that allow one to take snapshots of the cells' molecular profiles in a systematic fashion. Moreover recently, also phenotypic data, data on diseases, symptoms, patients, etc. are being collected at nation-wide level thus giving us another source of highly related (causal) 'big data'. This wealth of data is usually modeled as networks (aka binary relations, graphs or webs) of interactions, (including protein-protein, metabolic, signaling and transcription-regulatory interactions). The network model is a key view point leading to the uncovering of mesoscale phenomena, thus providing an essential bridge between the observable phenotypes and 'omics' underlying mechanisms. Moreover, network analysis is a powerful 'hypothesis generation' tool guiding the scientific cycle of 'data gathering', 'data interpretation, 'hypothesis generation' and 'hypothesis testing'. A major challenge in contemporary research is the synthesis of deep insights coming from network science with the wealth of data (often noisy, contradictory, incomplete and difficult to replicate) so to answer meaningful biological questions, in a quantifiable way using static and dynamic properties of biological networks. |
| inference biology: Issues in Applied Mathematics: 2011 Edition , 2012-01-09 Issues in Applied Mathematics / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Applied Mathematics. The editors have built Issues in Applied Mathematics: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Applied Mathematics in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Applied Mathematics: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/. |
| inference biology: Green Chemistry and Technology Mark Anthony Benvenuto, George Ruger, 2021-03-08 The 6th volume of Green Chemical Processing considers sustainable chemistry in the context of innovative and emerging technologies, explaining how they can support the “greening” of industry processes. The American Chemical Society’s 12 Principles of Green Chemistry are woven throughout this text as well as the series to which this book belongs. |
| inference biology: A Critical Reflection on Automated Science Marta Bertolaso, Fabio Sterpetti, 2020-02-05 This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book re-think and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution. The volume covers examples from various fields and areas, such as molecular biology, climate modeling, clinical medicine, and artificial intelligence. The explosion of technological tools and drivers for scientific research calls for a renewed understanding of the human character of science. This book aims precisely to contribute to such a renewed understanding of science. |
| inference biology: Bioinformatics for Biologists Pavel Pevzner, Ron Shamir, 2011-09-15 The computational education of biologists is changing to prepare students for facing the complex datasets of today's life science research. In this concise textbook, the authors' fresh pedagogical approaches lead biology students from first principles towards computational thinking. A team of renowned bioinformaticians take innovative routes to introduce computational ideas in the context of real biological problems. Intuitive explanations promote deep understanding, using little mathematical formalism. Self-contained chapters show how computational procedures are developed and applied to central topics in bioinformatics and genomics, such as the genetic basis of disease, genome evolution or the tree of life concept. Using bioinformatic resources requires a basic understanding of what bioinformatics is and what it can do. Rather than just presenting tools, the authors - each a leading scientist - engage the students' problem-solving skills, preparing them to meet the computational challenges of their life science careers. |
| inference biology: Advances in Molecular Ecology Gary R. Carvalho, 1998 Each contributor to this publication was asked to examine how molecular genetic tools have contributed to their specific areas of consideration. To increase the practical utility of the book, a summary of software that is available for the anaylsis of data in molecular ecology is included. |
| inference biology: Mathematical Approaches to Polymer Sequence Analysis and Related Problems Renato Bruni, 2010-10-17 An edited volume describing the latest developments in approaching the problem of polymer sequence analysis, with special emphasis on the most relevant biopolymers (peptides and DNA) but not limited to them. The chapters will include peptide sequence analysis, DNA sequence analysis, analysis of biopolymers and nonpolymers, sequence alignment problems, and more. |
| inference biology: Pattern Recognition in Bioinformatics Tjeerd M.H. Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes, 2010-09-20 This book constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition in Bioinformatics, PRIB 2010, held in Nijmegen, The Netherlands, in September 2010. The 38 revised full papers presented were carefully reviewed and selected from 46 submissions. The field of bioinformatics has two main objectives: the creation and maintenance of biological databases and the analysis of life sciences data in order to unravel the mysteries of biological function. Computer science methods such as pattern recognition, machine learning, and data mining have a great deal to offer the field of bioinformatics. |
| inference biology: Encyclopedia of Parallel Computing David Padua, 2011-09-08 Containing over 300 entries in an A-Z format, the Encyclopedia of Parallel Computing provides easy, intuitive access to relevant information for professionals and researchers seeking access to any aspect within the broad field of parallel computing. Topics for this comprehensive reference were selected, written, and peer-reviewed by an international pool of distinguished researchers in the field. The Encyclopedia is broad in scope, covering machine organization, programming languages, algorithms, and applications. Within each area, concepts, designs, and specific implementations are presented. The highly-structured essays in this work comprise synonyms, a definition and discussion of the topic, bibliographies, and links to related literature. Extensive cross-references to other entries within the Encyclopedia support efficient, user-friendly searchers for immediate access to useful information. Key concepts presented in the Encyclopedia of Parallel Computing include; laws and metrics; specific numerical and non-numerical algorithms; asynchronous algorithms; libraries of subroutines; benchmark suites; applications; sequential consistency and cache coherency; machine classes such as clusters, shared-memory multiprocessors, special-purpose machines and dataflow machines; specific machines such as Cray supercomputers, IBM’s cell processor and Intel’s multicore machines; race detection and auto parallelization; parallel programming languages, synchronization primitives, collective operations, message passing libraries, checkpointing, and operating systems. Topics covered: Speedup, Efficiency, Isoefficiency, Redundancy, Amdahls law, Computer Architecture Concepts, Parallel Machine Designs, Benmarks, Parallel Programming concepts & design, Algorithms, Parallel applications. This authoritative reference will be published in two formats: print and online. The online edition features hyperlinks to cross-references and to additional significant research. Related Subjects: supercomputing, high-performance computing, distributed computing |
| inference biology: Biotechnology Applications in Forestry Fred O Asiegbu, Andriy Kovalchuk, 2025-01-20 Biotechnology Applications in Forestry: Forest Microbiology, Volume Four in the Forest Microbiology series, is a comprehensive exploration of harnessing the unique attributes of the microbes in the forest biome and their tree hosts. The book introduces the basics of genomics, applied bioinformatics and next generation sequencing, providing a firm foundation before moving to specific approach, application and use chapters. Further sections explore opportunities through the use of genetics to expand or improve on many of these positive attributes of forest trees and associated organisms, including adaptation to climate change as well as resilience to biotic and abiotic stressors.Novel techniques and current advances in the application of modern biotechniques in tree health protection, mushroom technology, biological control, biochar, bioenergy, Isolate & strain selection, metabolic engineering and commercial application relevant for forest ecosystem are also addressed. - Outlines novel approaches in the use of fungi or bacteria for biocontrol of insect pests and invasive plant species - Highlights the many functions and uses of forest microbes as biofertilizers, in soil fertility, and in bioremediation, including phytoremediation - Addresses major industrial and biotechnological applications of forest microbes |
| inference biology: Evolutionary Multi-Criterion Optimization Shigeru Obayashi, 2007-02-12 This book constitutes the refereed proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2007, held in Matsushima, Japan in March 2007. The 65 revised full papers presented together with 4 invited papers are organized in topical sections on algorithm design, algorithm improvements, alternative methods, applications, engineering design, many objectives, objective handling, and performance assessments. |
| inference biology: Young Children's Thinking about Biological World Giyoo Hatano, Kayoko Inagaki, 2013-04-15 Presents research on the topic of young children's naive biology, examining such theoretical issues as processes, conditions and mechanisms in conceptual development using the development of biological understanding as the target case. |
| inference biology: Evolutionary Computation in Gene Regulatory Network Research Hitoshi Iba, Nasimul Noman, 2016-01-20 Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics. |
| inference biology: Algorithmic Aspects in Information and Management Andrew Goldberg, Yunhong Zhou, 2009-05-25 This book constitutes the proceedings of the 5th International Conference on Algorithmic Aspects in Information Management, AAIM 2009, held in San Francisco, CA, USA, in June 2009. The 25 papers presented together with the abstracts of two invited talks were carefully reviewed and selected for inclusion in this book. While the areas of information management and management science are full of algorithmic challenges, the proliferation of data (Internet, biology, finance etc) has called for the design of efficient and scalable algorithms and data structures for their management and processing. This conference is intended for original algorithmic research on immediate applications and/or fundamental problems pertinent to information management and management science, broadly construed. The conference aims at bringing together researchers in Computer Science, Operations Research, Economics, Game Theory, and related disciplines. |
| inference biology: Systems Medicine , 2020-08-24 Technological advances in generated molecular and cell biological data are transforming biomedical research. Sequencing, multi-omics and imaging technologies are likely to have deep impact on the future of medical practice. In parallel to technological developments, methodologies to gather, integrate, visualize and analyze heterogeneous and large-scale data sets are needed to develop new approaches for diagnosis, prognosis and therapy. Systems Medicine: Integrative, Qualitative and Computational Approaches is an innovative, interdisciplinary and integrative approach that extends the concept of systems biology and the unprecedented insights that computational methods and mathematical modeling offer of the interactions and network behavior of complex biological systems, to novel clinically relevant applications for the design of more successful prognostic, diagnostic and therapeutic approaches. This 3 volume work features 132 entries from renowned experts in the fields and covers the tools, methods, algorithms and data analysis workflows used for integrating and analyzing multi-dimensional data routinely generated in clinical settings with the aim of providing medical practitioners with robust clinical decision support systems. Importantly the work delves into the applications of systems medicine in areas such as tumor systems biology, metabolic and cardiovascular diseases as well as immunology and infectious diseases amongst others. This is a fundamental resource for biomedical students and researchers as well as medical practitioners who need to need to adopt advances in computational tools and methods into the clinical practice. Encyclopedic coverage: ‘one-stop’ resource for access to information written by world-leading scholars in the field of Systems Biology and Systems Medicine, with easy cross-referencing of related articles to promote understanding and further research Authoritative: the whole work is authored and edited by recognized experts in the field, with a range of different expertise, ensuring a high quality standard Digitally innovative: Hyperlinked references and further readings, cross-references and diagrams/images will allow readers to easily navigate a wealth of information |
| inference biology: Bayesian Evolutionary Analysis with BEAST Alexei J. Drummond, Remco R. Bouckaert, 2015-08-06 Covers theory, practice and programming in Bayesian phylogenetics with BEAST. The why, how and what of BEAST 2. |
| inference biology: Phylogeny and Conservation Andy Purvis, John L. Gittleman, Thomas M. Brooks, 2005-09-22 Phylogeny is a potentially powerful tool for conserving biodiversity. This book explores how it can be used to tackle questions of great practical importance and urgency for conservation. Using case studies from many different taxa and regions of the world, the volume evaluates how useful phylogeny is in understanding the processes that have generated today's diversity and the processes that now threaten it. The urgency with which conservation decisions have to be made as well as the need for the best possible decisions make this volume of great value to researchers, practitioners and policy-makers. |
| inference biology: Population Genetics and Microevolutionary Theory Alan R. Templeton, 2021-05-04 Population Genetics and Microevolutionary Theory Explore the fundamentals of the biological implications of population genetic theory In the newly revised Second Edition of Population Genetics and Microevolutionary Theory, accomplished researcher and author Alan R. Templeton delivers a fulsome discussion of population genetics with coverage of exciting new developments in the field, including new discoveries in epigenetics and genome-wide studies. The book prepares students to successfully apply population genetics analytical tools by providing a solid foundation in microevolutionary theory. The book emphasizes that population structure forms the underlying template upon which quantitative genetics and natural selection operate and is a must-read for future population and evolutionary geneticists and those who wish to work in genetic epidemiology or conservation biology. You’ll learn about a wide array of topics, including quantitative genetics, the interactions of natural selection with other evolutionary forces, and selection in heterogeneous environments and age-structured populations. Appendices that cover genetic survey techniques and probability and statistics conclude the book. Readers will also benefit from the inclusion of: A thorough introduction to population genetics, including the scope of the subject, its premises, and the Hardy-Weinberg Model of Microevolution An exploration of systems of mating, including a treatment of the use of runs of homozygosity to show pedigree inbreeding in distant ancestors A practical discussion of genetic drift, including the use of effective sizes in conservation biology (with a discussion of African rhinos as an example) A concise examination of coalescence, including a treatment of the infinite sites model Perfect for graduate students in genetics and evolutionary biology programs and advanced undergraduate biology majors, Population Genetics and Microevolutionary Theory will also earn a place in the libraries of students taking courses in conservation biology, human genetics, bioinformatics, and genomics. |
| inference biology: Inferring Phylogenies Joseph Felsenstein, 2004-01 Phylogenies, or evolutionary trees, are the basic structures necessary to think about and analyze differences between species. Statistical, computational, and algorithmic work in this field has been ongoing for four decades now, and there have been great advances in understanding. Yet no book has summarized this work. Inferring Phylogenies does just that in a single, compact volume. Phylogenies are inferred with various kinds of data. This book concentrates on some of the central ones: discretely coded characters, molecular sequences, gene frequencies, and quantitative traits. Also covered are restriction sites, RAPDs, and microsatellites. |
| inference biology: International Handbook of Research on Conceptual Change Stella Vosniadou, 2013-07-18 Conceptual change research investigates the processes through which learners substantially revise prior knowledge and acquire new concepts. Tracing its heritage to paradigms and paradigm shifts made famous by Thomas Kuhn, conceptual change research focuses on understanding and explaining learning of the most the most difficult and counter-intuitive concepts. Now in its second edition, the International Handbook of Research on Conceptual Change provides a comprehensive review of the conceptual change movement and of the impressive research it has spawned on students’ difficulties in learning. In thirty-one new and updated chapters, organized thematically and introduced by Stella Vosniadou, this volume brings together detailed discussions of key theoretical and methodological issues, the roots of conceptual change research, and mechanisms of conceptual change and learner characteristics. Combined with chapters that describe conceptual change research in the fields of physics, astronomy, biology, medicine and health, and history, this handbook presents writings on interdisciplinary topics written for researchers and students across fields. |
| inference biology: A Brief History of Blood and Lymphatic Vessels Andreas Bikfalvi, 2018-04-13 This book provides a comprehensive account of vascular biology and pathology and its significance for health and disease. It systematically and chronologically explains how we came to our current understanding of the vasculature and it ́s function today, and describes in an entertaining way the diverse flaws and turns in science and medicine from the past. It thereby offers a complete and well-studied history on vascular biology and medicine. The book has an easy-to-read style and is written for students as well as scientists, physicians and lecturers in the field of biomedicine, human physiology, cardiology and hematology. |
| inference biology: Reconstructing the Past Elliott Sober, 1991-02-05 Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships. For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science. Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments. |
| inference biology: Bioinformatics Database Systems Kevin Byron, Katherine G. Herbert, Jason T. L. Wang, 2016-12-19 Modern biological databases comprise not only data, but also sophisticated query facilities and bioinformatics data analysis tools. This book provides an exploration through the world of Bioinformatics Database Systems. The book summarizes the popular and innovative bioinformatics repositories currently available, including popular primary genetic and protein sequence databases, phylogenetic databases, structure and pathway databases, microarray databases and boutique databases. It also explores the data quality and information integration issues currently involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field. Biological data integration issues are also covered in-depth, and the book demonstrates how data integration can create new repositories to address the needs of the biological communities. It also presents typical data integration architectures employed in current bioinformatics databases. The latter part of the book covers biological data mining and biological data processing approaches using cloud-based technologies. General data mining approaches are discussed, as well as specific data mining methodologies that have been successfully deployed in biological data mining applications. Two biological data mining case studies are also included to illustrate how data, query, and analysis methods are integrated into user-friendly systems. Aimed at researchers and developers of bioinformatics database systems, the book is also useful as a supplementary textbook for a one-semester upper-level undergraduate course, or an introductory graduate bioinformatics course. About the Authors Kevin Byron is a PhD candidate in the Department of Computer Science at the New Jersey Institute of Technology. Katherine G. Herbert is Associate Professor of Computer Science at Montclair State University. Jason T.L. Wang is Professor of Bioinformatics and Computer Science at the New Jersey Institute of Technology. |
机器学习中Inference 和predict的区别是什么? - 知乎
Inference: You want to understand how ozone levels are influenced by temperature, solar radiation, and wind. Since you assume that the residuals are normally distributed, you use a …
如何简单易懂地理解变分推断 (variational inference)？ - 知乎
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large …
因果推断会是下一个AI热潮吗？ - 知乎
The Causal-Neural Connection: Expressiveness, Learnability, and Inference Kevin M Xia (Columbia University) · Kai-Zhan Lee (Columbia University) · Yoshua Bengio (University of …
统计学方面国内外有哪些权威的期刊？ - 知乎
国内暂时没有看到专门的统计的权威期刊，学科评审只列了经济研究 外文期刊中的四大天王 jrssb aos jasa biometrika 以及和四大天王并驾齐驱的 joe 这上面的五大期刊应该是统计学最为权威的 …
面板数据用聚类稳健标准误，结果变得不显著；而不用的情况下很 …
据我所知，英文顶刊对标准误的聚类要求是很严的，至少要聚类到核心解释变量层面，有的还会往更高层面聚类作为稳健性检验。国内较少对标准误的聚类做要求，部分中文顶刊文章都不说明 …
英文文献如何正确导出为参考文献格式？ - 知乎
英文专业的小伙伴们，还在为论文的参考文献格式苦恼和纠结吗？不必担心，小编都整理好了，实实在在的干货！ 英文参考文献格式一般来说有APA（美国心理学会American Psychological …
双向固定效应模型怎么理解？ - 知乎
那么，培训时长能够解释这一差异吗？ 这就是二维固定效应模型的意义。 参考资料： Scott Cunningham，Causal Inference The Mixtape Causal Inference for The Brave and True The …
Tesla P100和GTX 1080性能对比怎样？ - 知乎
训练神经网络， 1080ti 和P100没有太大差别。 有人说inference差很多，我不太相信但是也没做过实验。 总之你自费买卡做实验的话，没有任何理由买P100.
机器学习中Inference 和predict的区别是什么? - 知乎
Inference: You want to understand how ozone levels are influenced by temperature, solar radiation, and wind. Since you assume that the residuals are normally distributed, you use a …
如何简单易懂地理解变分推断 (variational inference)？ - 知乎
How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large …
因果推断会是下一个AI热潮吗？ - 知乎
The Causal-Neural Connection: Expressiveness, Learnability, and Inference Kevin M Xia (Columbia University) · Kai-Zhan Lee (Columbia University) · Yoshua Bengio (University of …
统计学方面国内外有哪些权威的期刊？ - 知乎
国内暂时没有看到专门的统计的权威期刊，学科评审只列了经济研究 外文期刊中的四大天王 jrssb aos jasa biometrika 以及和四大天王并驾齐驱的 joe 这上面的五大期刊应该是统计学最为权威的 …
面板数据用聚类稳健标准误，结果变得不显著；而不用的情况下很 …
据我所知，英文顶刊对标准误的聚类要求是很严的，至少要聚类到核心解释变量层面，有的还会往更高层面聚类作为稳健性检验。国内较少对标准误的聚类做要求，部分中文顶刊文章都不说明 …
英文文献如何正确导出为参考文献格式？ - 知乎
英文专业的小伙伴们，还在为论文的参考文献格式苦恼和纠结吗？不必担心，小编都整理好了，实实在在的干货！ 英文参考文献格式一般来说有APA（美国心理学会American Psychological …
双向固定效应模型怎么理解？ - 知乎
那么，培训时长能够解释这一差异吗？ 这就是二维固定效应模型的意义。 参考资料： Scott Cunningham，Causal Inference The Mixtape Causal Inference for The Brave and True The …
Tesla P100和GTX 1080性能对比怎样？ - 知乎
训练神经网络， 1080ti 和P100没有太大差别。 有人说inference差很多，我不太相信但是也没做过实验。 总之你自费买卡做实验的话，没有任何理由买P100.
