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  • 1
    UID:
    almahu_9949567210602882
    Umfang: XIX, 575 p. 162 illus., 156 illus. in color. , online resource.
    Ausgabe: 1st ed. 2023.
    ISBN: 9783031133398
    Inhalt: In recent years, large amounts of data became available in all areas of science, industry and society. This provides unprecedented opportunities for enhancing our knowledge, and to solve scientific and societal problems. In order to emphasize the importance of this, data have been called the "oil of the 21st Century". Unfortunately, data do usually not reveal information easily, but analysis methods are required to extract it. This is the main task of data science. The textbook provides students with tools they need to analyze complex data using methods from machine learning, artificial intelligence and statistics. These are the main fields comprised by data science. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. This allows the immediate practical application of the learning concepts side-by-side. The book advocates an integration of statistical thinking, computational thinking and mathematical thinking because data science is an interdisciplinary field requiring an understanding of statistics, computer science and mathematics. Furthermore, the book highlights the understanding of the domain knowledge about experiments or processes that generate or produce the data. The goal of the authors is to provide students with a systematic approach to data science that allows a continuation of the learning process beyond the presented topics. Hence, the book enables learning to learn. Main features of the book: - emphasizing the understanding of methods and underlying concepts - integrating statistical thinking, computational thinking and mathematical thinking - highlighting the understanding of the data - exploring the power of visualizations - balancing theoretical and practical presentations - demonstrating the application of methods using R - providing detailed examples and discussions - presenting data science as a complex network Elements of Data Science, Machine Learning and Artificial Intelligence using R presents basic, intermediate and advanced methods for learning from data, culminating into a practical toolbox for a modern data scientist. The comprehensive coverage allows a wide range of usages of the textbook from (advanced) undergraduate to graduate courses. .
    Anmerkung: Introduction -- Introduction to learning from data -- Part 1: General topics -- Prediction models -- Error measures -- Resampling -- Data types -- Part 2: Core methods -- Maximum Likelihood & Bayesian analysis -- Clustering -- Dimension Reduction -- Classification -- Hypothesis testing -- Linear Regression -- Model Selection -- Part 3: Advanced topics -- Regularization -- Deep neural networks -- Multiple hypothesis testing -- Survival analysis -- Generalization error -- Theoretical foundations -- Conclusion.
    In: Springer Nature eBook
    Weitere Ausg.: Printed edition: ISBN 9783031133381
    Weitere Ausg.: Printed edition: ISBN 9783031133404
    Weitere Ausg.: Printed edition: ISBN 9783031133411
    Sprache: Englisch
    URL: Volltext  (URL des Erstveröffentlichers)
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    UID:
    almahu_BV046203036
    Umfang: XV, 414 Seiten : , Illustrationen, Diagramme ; , 24 cm x 17 cm.
    ISBN: 978-3-11-056467-9 , 3-11-056467-X
    Serie: De Gruyter Oldenbourg STEM
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, PDF ISBN 978-3-11-056499-0
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-3-11-056502-7
    Sprache: Englisch
    Fachgebiete: Informatik
    RVK:
    RVK:
    RVK:
    RVK:
    Schlagwort(e): Data Science ; Mathematik ; R ; Data Mining ; Mathematik ; R ; Lehrbuch ; Lehrbuch
    Mehr zum Autor: Moutari, Salissou.
    Mehr zum Autor: Emmert-Streib, Frank.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 3
    UID:
    almahu_BV043829977
    Umfang: XVII, 343 Seiten : , Illustrationen, Diagramme (überwiegend farbig).
    ISBN: 978-3-527-33958-7
    Serie: Quantitative and network biology volume 7
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, oBook ISBN 978-3-527-69436-5
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, PDF ISBN 978-3-527-69440-2
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, ePub ISBN 978-3-527-69437-2
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, Mobi ISBN 978-3-527-69438-9
    Sprache: Englisch
    Fachgebiete: Informatik , Mathematik
    RVK:
    RVK:
    Schlagwort(e): Computational chemistry ; Systembiologie ; Medizinische Informatik ; R
    Mehr zum Autor: Emmert-Streib, Frank.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Weinheim, Germany :Wiley-Blackwell,
    UID:
    almafu_9959328266202883
    Umfang: 1 online resource (xx, 292 pages) : , illustrations (some color)
    Ausgabe: First edition.
    ISBN: 9783527665471 , 3527665471 , 9783527665440 , 3527665447 , 9783527665457 , 3527665455 , 9781299158511 , 129915851X , 9783527665464 , 3527665463
    Serie: Quantitative and network biology ; volume 3
    Inhalt: This title discusses different methods for statistically analyzing and validating data created with high-throughput methods. It focuses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network.
    Anmerkung: Edition statement from running title area. , Part one: General overview. Control of type I error rates for oncology biomarker discovery with high-throughput platforms -- Overview of public cancer databases, resources, and visualization tools -- Part two: Bayesian methods. Discovery of expression signatures in chronic myeloid leukemia by Bayesian model averaging -- Bayesian ranking and selection methods in microarray studies -- Multiclass classification via Bayesian variable selection with gene expression data -- Semisupervised methods for analyzing high-dimensional genomic data -- Part three: Network-based approaches -- Colorectal cancer and its molecular subsystems: construction, interpretation, and validation -- Network medicine: disease genes in molecular networks -- Inference of gene regulatory networks in breast and ovarian cancer by integrating different genomic data -- Network-module-based approaches in cancer data analysis -- Discriminant and network analysis to study origin of cancer -- Intervention and control of gene regulatory networks: theoretical framework and application to human melanoma gene regulation -- Part four: Phenotype influence of DNA copy number aberrations. Identification of recurrent DNA copy number aberrations in tumors -- The cancer cell, its entropy, and high-dimensional molecular data.
    Weitere Ausg.: Print version: Statistical diagnostics for cancer. Weinheim, germany : Wiley-Blackwell, [2013] ISBN 9783527332625
    Sprache: Englisch
    Schlagwort(e): Electronic books. ; Electronic books. ; Electronic books.
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  • 5
    Online-Ressource
    Online-Ressource
    Weinheim :Wiley-VCH ;
    UID:
    almafu_9959327635502883
    Umfang: 1 online resource (400 pages)
    ISBN: 9783527630332 , 3527630333 , 9783527325856 , 3527325859 , 9783527630349 , 3527630341 , 1282687786 , 9781282687783
    Inhalt: A collection of highly valuable statistical and computational approaches designed for developing powerful methods to analyze large-scale high-throughput data derived from studies of complex diseases. Such diseases include cancer and cardiovascular disease, and constitute the major health challenges in industrialized countries. They are characterized by the systems properties of gene networks and their interrelations, instead of individual genes, whose malfunctioning manifests in pathological phenotypes, thus making the analysis of the resulting large data sets particularly challenging. This is.
    Anmerkung: Front Matter -- General Biological and Statistical Basics. The Biology of in Health and Disease: A High Altitude View / Brian C Turner, Gregory A Bird, Yosef Refaeli -- Cancer Stem Cells : Finding and Capping the Roots of Cancer / Eike C Buss, Anthony D Ho -- Multiple Testing Methods / Alessio Farcomeni -- Statistical and Computational Analysis Methods. Making Mountains out of Molehills: Moving from Single Gene to Pathway Based Models of Colon Cancer Progression / Elena Edelman, Katherine Garman, Anil Potti, Sayan Mukherjee -- Gene-Set Expression Analysis: Challenges and Tools / Assaf P Oron -- Multivariate Analysis of Microarray Data Using Hotelling's Test / Yan Lu, Peng-Yuan Liu, Hong-Wen Deng -- Interpreting Differential Coexpression of Gene Sets / Ju Han Kim, Sung Bum Cho, Jihun Kim -- Multivariate Analysis of Microarray Data: Application of MANOVA / Taeyoung Hwang, Taesung Park -- Testing Significance of a Class of Genes / James J Chen, Chen-An Tsai -- Differential Dependency Network Analysis to Identify Topological Changes in Biological Networks / Bai Zhang, Huai Li, Robert Clarke, Leena Hilakivi-Clarke, Yue Wang -- An Introduction to Time-Varying Connectivity Estimation for Gene Regulatory Networks / Andř Fujita, Joo Ricardo Sato, Marcos Angelo Almeida Demasi, Satoru Miyano, Mari Cleide Sogayar, Carlos Eduardo Ferreira -- A Systems Biology Approach to Construct a Cancer-Perturbed Protein-Protein Interaction Network for Apoptosis by Means of Microarray and Database Mining / Liang-Hui Chu, Bor-Sen Chen -- A New Gene Expression Meta-Analysis Technique and its Application to Co-Analyze Three Independent Lung Cancer Datasets / Irit Fishel, Alon Kaufman, Eytan Ruppin -- Kernel Classification Methods for Cancer Microarray Data / Tsuyoshi Kato, Wataru Fujibuchi -- Predicting Cancer Survival Using Expression Patterns / Anupama Reddy, Louis-Philippe Kronek, A Rose Brannon, Michael Seiler, Shridar Ganesan, W Kimryn Rathmell, Gyan Bhanot -- Integration of Microarray Datasets / Ki-Yeol Kim, Sun Young Rha -- Model Averaging for Biological Networks with Prior Information / Sach Mukherjee, Terence P Speed, Steven M Hill.
    Weitere Ausg.: Print version: Medical biostatistics for complex diseases. Weinheim : Wiley-VCH ; Chichester : John Wiley [distributor], 2010 ISBN 9783527325856
    Weitere Ausg.: ISBN 3527325859
    Sprache: Englisch
    Schlagwort(e): Electronic books. ; Electronic books. ; Electronic books.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Weinheim :Wiley-VCH,
    UID:
    almafu_9959327545302883
    Umfang: 1 online resource (xviii, 462 pages) : , illustrations (some color)
    ISBN: 9783527627998 , 3527627995 , 9783527627981 , 3527627987
    Inhalt: Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.
    Anmerkung: Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 2 Statistical Mechanics of Complex Networks; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 6 Graph Edit Distance -- Optimal and Suboptimal Algorithms with Applications; 7 Graph Energy; 8 Generalized Shortest Path Trees: A Novel Graph Class by Example of Semiotic Networks.
    Weitere Ausg.: Print version: Analysis of complex networks. Weinheim : Wiley-VCH, ©2009 ISBN 9783527323456
    Sprache: Englisch
    Schlagwort(e): Electronic books. ; Electronic books. ; Electronic books.
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  • 7
    UID:
    almahu_9949319879602882
    Umfang: 1 online resource (516 pages) : , illustrations.
    ISBN: 9781466584525 (e-book)
    Serie: Discrete mathematics and its applications
    Inhalt: "Graph-based approaches have been employed extensively in several disciplines such as biology, computer science, chemistry, and so forth. In the 1990s, exploration of the topology of complex networks became quite popular and was triggered by the breakthrough of the Internet and the examinations of random networks. As a consequence, the structure of random networks has been explored using graph-theoretic methods and stochastic growth models. However, it turned out that besides exploring random graphs, quantitative approaches to analyze networks are crucial as well. This relates to quantifying structural information of complex networks by using ameasurement approach. As demonstrated in the scientific literature, graph- and informationtheoretic measures, and statistical techniques applied to networks have been used to do this quantification. It has been found that many real-world networks are composed of network patterns representing nonrandom topologies.Graph- and information-theoretic measures have been proven efficient in quantifying the structural information of such patterns. The study of relevant literature reveals that quantitative graph theory has not yet been considered a branch of graph theory"--
    Weitere Ausg.: Print version: Quantitative graph theory : mathematical foundations and applications. Boca Raton : CRC Press, [2015] ISBN 9781466584518
    Sprache: Englisch
    Fachgebiete: Mathematik
    RVK:
    Schlagwort(e): Electronic books.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 8
    Buch
    Buch
    Boca Raton : CRC Press, Taylor & Francis Group, CRC Press is an imprint of the Taylor & Francis Group, an informa business
    UID:
    b3kat_BV045001819
    Umfang: x, 383 Seiten , Illustrationen , 24 cm
    ISBN: 9781498799324
    Serie: Chapman & Hall/CRC big data series
    Anmerkung: Auf dem Umschlag: "A Chapman & Hall book". - Includes bibliographical references and index
    Sprache: Englisch
    Mehr zum Autor: Dehmer, Matthias 1968-
    Mehr zum Autor: Emmert-Streib, Frank
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 9
    UID:
    almahu_BV042715709
    Umfang: XXXIV, 242 Seiten : , Diagramme (teilweise farbig).
    ISBN: 978-3-527-33724-8 , 978-3-527-69151-7
    Serie: Quantitative and network biology volume 5
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-3-527-69154-8
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, MOBI ISBN 978-3-527-69152-4
    Weitere Ausg.: Erscheint auch als Online-Ausgabe, PDF ISBN 978-3-527-69153-1
    Sprache: Englisch
    Fachgebiete: Informatik
    RVK:
    Schlagwort(e): Netzwerktheorie ; Computersimulation
    Mehr zum Autor: Pickl, Stefan 1967-
    Mehr zum Autor: Emmert-Streib, Frank.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 10
    Buch
    Buch
    Weinheim :Wiley-VCH-Verl.,
    UID:
    almahu_BV023308521
    Umfang: XX, 418 S. : , Ill., graph. Darst.
    ISBN: 978-3-527-31822-3
    Sprache: Englisch
    Fachgebiete: Biologie
    RVK:
    RVK:
    Schlagwort(e): Microarray ; Datenanalyse
    Mehr zum Autor: Emmert-Streib, Frank.
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