Data Mining for Systems Biology
Author | : Hiroshi Mamitsuka |
Publisher | : |
Total Pages | : 243 |
Release | : 2018 |
Genre | : Data mining |
ISBN | : 9781493985616 |
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Author | : Hiroshi Mamitsuka |
Publisher | : |
Total Pages | : 243 |
Release | : 2018 |
Genre | : Data mining |
ISBN | : 9781493985616 |
Author | : Jake Y. Chen |
Publisher | : CRC Press |
Total Pages | : 736 |
Release | : 2009-09-01 |
Genre | : Computers |
ISBN | : 1420086855 |
Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin
Author | : Hiroshi Mamitsuka |
Publisher | : Humana |
Total Pages | : 243 |
Release | : 2019-08-04 |
Genre | : Science |
ISBN | : 9781493993260 |
This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.
Author | : Yonghong Peng |
Publisher | : |
Total Pages | : |
Release | : 2007 |
Genre | : Biological systems |
ISBN | : |
Author | : Bor-Sen Chen |
Publisher | : Academic Press |
Total Pages | : 880 |
Release | : 2016-10-25 |
Genre | : Science |
ISBN | : 0128097078 |
Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource. This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements. The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research. Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification Provides more than 140 diagrams to illustrate Big Mechanism in systems biology Presents worked examples in each chapter
Author | : Hui-Huang Hsu |
Publisher | : IGI Global |
Total Pages | : 343 |
Release | : 2006-01-01 |
Genre | : Computers |
ISBN | : 1591408636 |
"This book covers research topics of data mining on bioinformatics presenting the basics and problems of bioinformatics and applications of data mining technologies pertaining to the field"--Provided by publisher.
Author | : Elisabeth Georgii |
Publisher | : |
Total Pages | : 0 |
Release | : 2010 |
Genre | : |
ISBN | : |
Author | : Avraam Tapinos |
Publisher | : |
Total Pages | : |
Release | : 2013 |
Genre | : |
ISBN | : |
Analysis of time series data constitutes an important activity in many scientific disciplines. Over the last years there has been an increase in the collection of time series data in all scientific fields and disciplines, such as the industry and engineering. Due to the increasing size of the time series datasets, new automated time series data mining techniques have been devised for comparing time series data and present information in a logical and easily comprehensible structure. In systems biology in particular, time series are used to the study biological systems. The time series representations of a systems' dynamics behaviour are multivariate time series. Time series are considered multivariate when they contain observations for more than one variable component. The biological systems' dynamics time series contain observations for every feature component that is included in the system; they thus are multivariate time series. Recently, there has been an increasing interest in the collection of biological time series. It would therefore be beneficial for systems biologist to be able to compare these multivariate time series. Over the last decade, the field of time series analysis has attracted the attention of people from different scientific disciplines. A number of researchers from the data mining community focus their efforts on providing solutions on numerous problems regarding different time series data mining tasks. Different methods have been proposed for instance, for comparing, indexing and clustering, of univariate time series. Furthermore, different methods have been proposed for creating abstract representations of time series data and investigating the benefits of using these representations for data mining tasks. The introduction of more advanced computing resources facilitated the collection of multivariate time series, which has become common practise in various scientific fields. The increasing number of multivariate time series data triggered the demand for methods to compare them. A small number of well-suited methods have been proposed for comparing these multivariate time series data. All the currently available methods for multivariate time series comparison are more than adequate for comparing multivariate time series with the same dimensionality. However, they all suffer the same drawback. Current techniques cannot process multivariate time series with different dimensions. A proposed solution for comparing multivariate time series with arbitrary dimensions requires the creation of weighted averages. However, the accumulation of weights data is not always feasible. In this project, a new method is proposed which enables the comparison of multivariate time series with arbitrary dimensions. The particular method is evaluated on multivariate time series from different disciplines in order to test the methods' applicability on data from different fields of science and industry. Lastly, the newly formed method is applied to perform different time series data mining analyses on a set of biological data.
Author | : Steve Horvath |
Publisher | : Springer Science & Business Media |
Total Pages | : 433 |
Release | : 2011-04-30 |
Genre | : Science |
ISBN | : 144198819X |
High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.
Author | : Michael Stumpf |
Publisher | : John Wiley & Sons |
Total Pages | : 624 |
Release | : 2011-09-09 |
Genre | : Science |
ISBN | : 1119952042 |
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.