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Author : Bernhard Sch?lkopf
Publisher : Unknown
Page : 128 pages
File Size : 51,6 Mb
Release : 2016
Category : Electronic
ISBN : 0262292688
Author : Bernhard Schölkopf,Koji Tsuda,Jean-Philippe Vert
Publisher : MIT Press
Page : 428 pages
File Size : 50,5 Mb
Release : 2004
Category : Computers
ISBN : 0262195097
A detailed overview of current research in kernel methods and their application to computational biology.
Author : Gustavo Camps-Valls,José Luis Rojo-Álvarez,Manel Martínez-Ramón
Publisher : IGI Global
Page : 431 pages
File Size : 50,9 Mb
Release : 2007-01-01
Category : Technology & Engineering
ISBN : 9781599040424
"This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.
Author : Anonim
Publisher : Elsevier
Page : 3421 pages
File Size : 43,7 Mb
Release : 2018-08-21
Category : Medical
ISBN : 9780128114322
Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases
Author : Huma M. Lodhi,Stephen H. Muggleton
Publisher : John Wiley & Sons
Page : 435 pages
File Size : 44,8 Mb
Release : 2010-03-25
Category : Computers
ISBN : 9780470556740
Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.
Author : Henry Horng Lu,Bernhard Sch Lkopf,Hongyu Zhao
Publisher : Unknown
Page : 640 pages
File Size : 53,9 Mb
Release : 2011-05-19
Category : Electronic
ISBN : 3642163467
Author : Emmanuel Barillot,Laurence Calzone,Philippe Hupe,Jean-Philippe Vert,Andrei Zinovyev
Publisher : CRC Press
Page : 463 pages
File Size : 54,6 Mb
Release : 2012-08-25
Category : Science
ISBN : 9781439831441
The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models—integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencing. Offering promising insights on how to defeat cancer, the emerging field of systems biology captures the complexity of biological phenomena using mathematical and computational tools. Novel Approaches to Fighting Cancer Drawn from the authors’ decade-long work in the cancer computational systems biology laboratory at Institut Curie (Paris, France), Computational Systems Biology of Cancer explains how to apply computational systems biology approaches to cancer research. The authors provide proven techniques and tools for cancer bioinformatics and systems biology research. Effectively Use Algorithmic Methods and Bioinformatics Tools in Real Biological Applications Suitable for readers in both the computational and life sciences, this self-contained guide assumes very limited background in biology, mathematics, and computer science. It explores how computational systems biology can help fight cancer in three essential aspects: Categorising tumours Finding new targets Designing improved and tailored therapeutic strategies Each chapter introduces a problem, presents applicable concepts and state-of-the-art methods, describes existing tools, illustrates applications using real cases, lists publically available data and software, and includes references to further reading. Some chapters also contain exercises. Figures from the text and scripts/data for reproducing a breast cancer data analysis are available at www.cancer-systems-biology.net.
Author : Holger Fröhlich
Publisher : Logos Verlag Berlin
Page : 0 pages
File Size : 50,9 Mb
Release : 2006
Category : Bioinformatics
ISBN : 3832514392
This thesis is devoted to the finding of possible solutions for some machine learning related problems in modern chemo- and bioinformatics by means of so-called kernel methods. They are a special family of learning algorithms that have attracted a growing interest during the last years due to their good theoretical foundation and many successful practical applications in various disciplines. At the core of all kernel methods is the usage of a kernel function, which can be thought of as a special similarity measure between arbitrary objects. At the beginning of this thesis fundamentals and principles of kernel machines are reviewed. Afterwards a novel algorithm for model selection for Support Vector Machines (SVMs) in classification and regression is proposed, which is based on ideas from global optimization theory. It does not make any assumptions about special properties of the kernel function, like differentiability, and is highly efficient. Experimental comparisons to existing algorithms yield good results. After this we turn our point of interest to applications of kernel methods in chemo- and bioinformatics: For the ADME in silico prediction problem in modern drug discovery descriptor and graph-based representations of molecules are investigated. A descriptor selection algorithm is proposed, which can improve the statistical stability of an existing method. Furthermore, a novel class of specialized kernel functions is introduced that allows the comparison of a pair of molecules on a graph-based level. Various combinations of graph and descriptor-based representations are investigated, which on one hand allow the incorporation of expert domain knowledge and on the other hand the integration of different notions of molecular similarity in one SVM model. Furthermore, a reduced graph representation for molecular structures is proposed, in which certain structural elements are condensed in one node of the graph. Our experiments indicate that with our method improvements of the prediction performance compared to state-of-the-art modelling approaches can be achieved. At the same time our method is computationally rather cheap, unified and highly flexible. Another question, that is examined in the content of this thesis, is, which features of the membrane potentiel (MP) determine the generation of action potentials (APs) in cortical neurons in vivo. SVMs are trained to predict the occurrence of an AP before its onset based on several extracted features of the MP. A specialized feature selection algorithm is then used to select the most important features simultaneously in several in vivo recordings. In conclusion we find that the occurrence of an AP not only depends on the value of the MP shortly before AP onset, but also on the MP rate of change, the increase of the membrane potential several ms before AP onset, and the long range mean MP. Our findings systematically extend investigations by other researchers and are partially also confirmed by their results. As a last application of kernel methods in this thesis, we deal with the problem of clustering genes with regard to their function based on their Gene Ontology (GO) annotation. For this purpose specialized kernel functions are developed, which measure the similarity between gene products with respect to the structure of the GO graph. Using several clustering algorithms, like kernel k-means, spectral clustering and average linkage, we can detect meaningful clusters with our method. Applications to other ontologies or taxonomies in principle are possible.
Author : John Shawe-Taylor,Nello Cristianini
Publisher : Cambridge University Press
Page : 520 pages
File Size : 40,5 Mb
Release : 2004-06-28
Category : Computers
ISBN : 0521813972
Publisher Description
Author : Bernhard Scholkopf,Alexander J. Smola
Publisher : MIT Press
Page : 645 pages
File Size : 54,8 Mb
Release : 2018-06-05
Category : Computers
ISBN : 9780262536578
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Author : Tan, Joseph
Publisher : IGI Global
Page : 2772 pages
File Size : 40,5 Mb
Release : 2008-09-30
Category : Education
ISBN : 9781605660516
Provides a collection of medical IT research in topics such as clinical knowledge management, medical informatics, mobile health and service delivery, and gene expression.
Author : Bernhard Schölkopf,Alexander J. Smola
Publisher : MIT Press
Page : 658 pages
File Size : 45,9 Mb
Release : 2002
Category : Computers
ISBN : 0262194759
A comprehensive introduction to Support Vector Machines and related kernel methods.
Author : Henry Horng-Shing Lu,Bernhard Schölkopf,Hongyu Zhao
Publisher : Springer Science & Business Media
Page : 621 pages
File Size : 43,7 Mb
Release : 2011-05-17
Category : Mathematics
ISBN : 9783642163456
Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.
Author : Daskalaki, Andriani
Publisher : IGI Global
Page : 982 pages
File Size : 55,7 Mb
Release : 2008-11-30
Category : Technology & Engineering
ISBN : 9781605660776
"This book highlights the use of systems approaches including genomic, cellular, proteomic, metabolomic, bioinformatics, molecular, and biochemical, to address fundamental questions in complex diseases like cancer diabetes but also in ageing"--Provided by publisher.
Author : Ross Carlson,Herbert Sauro
Publisher : MDPI
Page : 214 pages
File Size : 41,5 Mb
Release : 2019-07-03
Category : Science
ISBN : 9783039211630
Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections: • Reviews of Computational Methods • Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels • The Interface of Biotic and Abiotic Processes • Processing of Large Data Sets for Enhanced Analysis • Parameter Optimization and Measurement
