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Statistical Modeling And Machine Learning For Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology PDF
Author: Alan Moses
Publisher: CRC Press
ISBN: 1482258609
Size: 76.10 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 264
View: 7491

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Statistical Modeling And Machine Learning For Molecular Biology

by Alan Moses, Statistical Modeling And Machine Learning For Molecular Biology Books available in PDF, EPUB, Mobi Format. Download Statistical Modeling And Machine Learning For Molecular Biology books, Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.




Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques  Tools  and Applications PDF
Author: K. G. Srinivasa
Publisher: Springer Nature
ISBN: 9811524459
Size: 62.82 MB
Format: PDF, Mobi
Category : Technology & Engineering
Languages : en
Pages : 317
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Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications

by K. G. Srinivasa, Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications Books available in PDF, EPUB, Mobi Format. Download Statistical Modelling And Machine Learning Principles For Bioinformatics Techniques Tools And Applications books, 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.




Bioinformatics And Computational Biology Solutions Using R And Bioconductor

Bioinformatics and Computational Biology Solutions Using R and Bioconductor PDF
Author: Robert Gentleman
Publisher: Springer Science & Business Media
ISBN: 0387293620
Size: 61.40 MB
Format: PDF
Category : Computers
Languages : en
Pages : 474
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Bioinformatics And Computational Biology Solutions Using R And Bioconductor

by Robert Gentleman, Bioinformatics And Computational Biology Solutions Using R And Bioconductor Books available in PDF, EPUB, Mobi Format. Download Bioinformatics And Computational Biology Solutions Using R And Bioconductor books, 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.




Statistical And Machine Learning Approaches For Network Analysis

Statistical and Machine Learning Approaches for Network Analysis PDF
Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 1118347013
Size: 62.40 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 344
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Statistical And Machine Learning Approaches For Network Analysis

by Matthias Dehmer, Statistical And Machine Learning Approaches For Network Analysis Books available in PDF, EPUB, Mobi Format. Download Statistical And Machine Learning Approaches For Network Analysis books, Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.




Stanford Bulletin

Stanford Bulletin PDF
Author:
Publisher:
ISBN:
Size: 37.21 MB
Format: PDF, Docs
Category :
Languages : en
Pages :
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Stanford Bulletin

by , Stanford Bulletin Books available in PDF, EPUB, Mobi Format. Download Stanford Bulletin books,




Statistical Modelling Of Molecular Descriptors In Qsar Qspr

Statistical Modelling of Molecular Descriptors in QSAR QSPR PDF
Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 3527645012
Size: 30.47 MB
Format: PDF, ePub
Category : Medical
Languages : en
Pages : 456
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Statistical Modelling Of Molecular Descriptors In Qsar Qspr

by Matthias Dehmer, Statistical Modelling Of Molecular Descriptors In Qsar Qspr Books available in PDF, EPUB, Mobi Format. Download Statistical Modelling Of Molecular Descriptors In Qsar Qspr books, 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.




Learning And Inference In Computational Systems Biology

Learning and Inference in Computational Systems Biology PDF
Author: Neil D. Lawrence
Publisher: Computational Molecular Biolog
ISBN:
Size: 73.70 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 362
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Learning And Inference In Computational Systems Biology

by Neil D. Lawrence, Learning And Inference In Computational Systems Biology Books available in PDF, EPUB, Mobi Format. Download Learning And Inference In Computational Systems Biology books, 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