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Statistical Modeling And Machine Learning For Molecular Biology
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Book Synopsis Statistical Modeling and Machine Learning for Molecular Biology by : Alan Moses
Download or read book Statistical Modeling and Machine Learning for Molecular Biology written by Alan Moses and published by CRC Press. This book was released on 2017-01-06 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: • Assumes no background in statistics or computers • Covers most major types of molecular biological data • Covers the statistical and machine learning concepts of most practical utility (P-values, clustering, regression, regularization and classification) • Intended for graduate students beginning careers in molecular biology, systems biology, bioengineering and genetics
Book Synopsis Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications by : K. G. Srinivasa
Download or read book Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications written by K. G. Srinivasa and published by Springer Nature. This book was released on 2020-01-30 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Book Synopsis Gene Expression Data Analysis by : Pankaj Barah
Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-21 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences
Book Synopsis Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications by : K. G. Srinivasa
Download or read book Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications written by K. G. Srinivasa and published by . This book was released on 2020 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.
Book Synopsis Statistical Modelling of Molecular Descriptors in QSAR/QSPR by : Matthias Dehmer
Download or read book Statistical Modelling of Molecular Descriptors in QSAR/QSPR written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-09-13 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.
Book Synopsis Bioconductor Case Studies by : Florian Hahne
Download or read book Bioconductor Case Studies written by Florian Hahne and published by Springer Science & Business Media. This book was released on 2010-06-09 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis. Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.
Book Synopsis Bioinformatics and Computational Biology Solutions Using R and Bioconductor by : Robert Gentleman
Download or read book Bioinformatics and Computational Biology Solutions Using R and Bioconductor written by Robert Gentleman and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
Book Synopsis Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology by : Kumar Selvarajoo
Download or read book Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology written by Kumar Selvarajoo and published by Springer Nature. This book was released on 2022-10-13 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides protocols for computational, statistical, and machine learning methods that are mainly applied to the study of metabolic engineering, synthetic biology, and disease applications. These techniques support the latest progress in cross-disciplinary research that integrates the different scales of biological complexity. The topics covered in this book are geared toward researchers with a background in engineering, computational analytical, and modeling experience and cover a broad range of topics in computational and machine learning approaches. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and practical, Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology is a valuable resource for any researcher or scientist who wants to learn more about the latest computational methods and how they are applied toward the understanding and prediction of complex biology.
Download or read book Bioinformatics written by Pierre Baldi and published by MIT Press (MA). This book was released on 1998 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Biotechnology, pharmacology, and medicine will be particularly affected by the new results and the increased understanding of life at the molecular level. Bioinformatics is the development and application of computer methods for analysis, interpretation, and prediction, as well as for the design of experiments. It has emerged as a strategic frontier between biology and computer science. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory—and this is exactly the situation in molecular biology. As with its predecessor, statistical model fitting, the goal in machine learning is to extract useful information from a body of data by building good probabilistic models. The particular twist behind machine learning, however, is to automate the process as much as possible. In this book, Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.
Book Synopsis Modern Statistics for Modern Biology by : SUSAN. HUBER HOLMES (WOLFGANG.)
Download or read book Modern Statistics for Modern Biology written by SUSAN. HUBER HOLMES (WOLFGANG.) and published by Cambridge University Press. This book was released on 2018 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Jaroslaw Meller Publisher :Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften ISBN 13 : Total Pages :136 pages Book Rating :4.F/5 ( download)
Book Synopsis Applications of Statistical and Machine Learning Methods in Bioinformatics by : Jaroslaw Meller
Download or read book Applications of Statistical and Machine Learning Methods in Bioinformatics written by Jaroslaw Meller and published by Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften. This book was released on 2007 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical and machine learning approaches play an increasingly important role in biomedical research. In the absence of fundamental (first principle-based) models, or because of the computational complexity of such models, statistical and machine learning approaches are being used to identify interesting structures in the data (e.g. patterns in gene expression profiles), correlate these patterns and other «input» attributes with (e.g. medically) relevant outcomes, and to develop predictors that can generalize from known data and make predictions for new data instances. Examples of important applications include structural bioinformatics, in which one of the goals is to predict elements of protein structure from amino acid sequence, or microarray gene expression profiling, in which the goal is to discover interesting patterns in gene expression data and correlate them with clinically relevant phenotypes. This volume includes papers submitted to the BIT 2005 workshop on the Applications of Machine and Statistical Learning Methods in Bioinformatics that took place in September 2005 in Torun, Poland.
Book Synopsis Statistical Modeling in Biomedical Research by : Yichuan Zhao
Download or read book Statistical Modeling in Biomedical Research written by Yichuan Zhao and published by Springer Nature. This book was released on 2020-03-19 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its applications.
Book Synopsis Introduction to Machine Learning and Bioinformatics by : Sushmita Mitra
Download or read book Introduction to Machine Learning and Bioinformatics written by Sushmita Mitra and published by CRC Press. This book was released on 2008-06-05 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.
Book Synopsis Probabilistic Modeling in Bioinformatics and Medical Informatics by : Dirk Husmeier
Download or read book Probabilistic Modeling in Bioinformatics and Medical Informatics written by Dirk Husmeier and published by Springer Science & Business Media. This book was released on 2006-05-06 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
Book Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Book Synopsis Mathematical Models of Plant-Herbivore Interactions by : Zhilan Feng
Download or read book Mathematical Models of Plant-Herbivore Interactions written by Zhilan Feng and published by CRC Press. This book was released on 2017-09-07 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Models of Plant-Herbivore Interactions addresses mathematical models in the study of practical questions in ecology, particularly factors that affect herbivory, including plant defense, herbivore natural enemies, and adaptive herbivory, as well as the effects of these on plant community dynamics. The result of extensive research on the use of mathematical modeling to investigate the effects of plant defenses on plant-herbivore dynamics, this book describes a toxin-determined functional response model (TDFRM) that helps explains field observations of these interactions. This book is intended for graduate students and researchers interested in mathematical biology and ecology.
Book Synopsis Bayesian Modeling in Bioinformatics by : Dipak K. Dey
Download or read book Bayesian Modeling in Bioinformatics written by Dipak K. Dey and published by CRC Press. This book was released on 2010-09-03 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c
