High-Dimensional Data Analysis in Cancer Research

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Publisher : Springer Science & Business Media
ISBN 13 : 0387697659
Total Pages : 164 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis High-Dimensional Data Analysis in Cancer Research by : Xiaochun Li

Download or read book High-Dimensional Data Analysis in Cancer Research written by Xiaochun Li and published by Springer Science & Business Media. This book was released on 2008-12-19 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

High-Dimensional Data Analysis in Cancer Research

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Publisher : Springer
ISBN 13 : 9780387697635
Total Pages : 392 pages
Book Rating : 4.6/5 (976 download)

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Book Synopsis High-Dimensional Data Analysis in Cancer Research by : Xiaochun Li

Download or read book High-Dimensional Data Analysis in Cancer Research written by Xiaochun Li and published by Springer. This book was released on 2008-12-12 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

High-Dimensional Data Analysis in Cancer Research

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Publisher : Springer
ISBN 13 : 9780387565125
Total Pages : 0 pages
Book Rating : 4.5/5 (651 download)

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Book Synopsis High-Dimensional Data Analysis in Cancer Research by : Xiaochun Li

Download or read book High-Dimensional Data Analysis in Cancer Research written by Xiaochun Li and published by Springer. This book was released on 2008-11-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

High-Dimensional Single Cell Analysis

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Publisher : Springer
ISBN 13 : 364254827X
Total Pages : 224 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis High-Dimensional Single Cell Analysis by : Harris G. Fienberg

Download or read book High-Dimensional Single Cell Analysis written by Harris G. Fienberg and published by Springer. This book was released on 2014-04-22 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume highlights the most interesting biomedical and clinical applications of high-dimensional flow and mass cytometry. It reviews current practical approaches used to perform high-dimensional experiments and addresses key bioinformatic techniques for the analysis of data sets involving dozens of parameters in millions of single cells. Topics include single cell cancer biology; studies of the human immunome; exploration of immunological cell types such as CD8+ T cells; decipherment of signaling processes of cancer; mass-tag cellular barcoding; analysis of protein interactions by proximity ligation assays; Cytobank, a platform for the analysis of cytometry data; computational analysis of high-dimensional flow cytometric data; computational deconvolution approaches for the description of intracellular signaling dynamics and hyperspectral cytometry. All 10 chapters of this book have been written by respected experts in their fields. It is an invaluable reference book for both basic and clinical researchers.

High-dimensional Microarray Data Analysis

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Publisher : Springer
ISBN 13 : 9811359989
Total Pages : 419 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis High-dimensional Microarray Data Analysis by : Shuichi Shinmura

Download or read book High-dimensional Microarray Data Analysis written by Shuichi Shinmura and published by Springer. This book was released on 2019-05-14 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.

Statistical Diagnostics for Cancer

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Publisher : John Wiley & Sons
ISBN 13 : 3527665455
Total Pages : 301 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis Statistical Diagnostics for Cancer by : Matthias Dehmer

Download or read book Statistical Diagnostics for Cancer written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-11-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses 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. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

High-dimensional Data Analysis

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Publisher :
ISBN 13 : 9787894236326
Total Pages : 318 pages
Book Rating : 4.2/5 (363 download)

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Book Synopsis High-dimensional Data Analysis by : Tony Cai;Xiaotong Shen

Download or read book High-dimensional Data Analysis written by Tony Cai;Xiaotong Shen and published by . This book was released on with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few years, significant developments have been taking place in highdimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from highdimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, cla.

Statistical Analysis for High-Dimensional Data

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Publisher : Springer
ISBN 13 : 3319270990
Total Pages : 313 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Statistical Analysis for High-Dimensional Data by : Arnoldo Frigessi

Download or read book Statistical Analysis for High-Dimensional Data written by Arnoldo Frigessi and published by Springer. This book was released on 2016-02-16 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

Analysis of Multivariate and High-Dimensional Data

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Publisher : Cambridge University Press
ISBN 13 : 0521887933
Total Pages : 531 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Analysis of Multivariate and High-Dimensional Data by : Inge Koch

Download or read book Analysis of Multivariate and High-Dimensional Data written by Inge Koch and published by Cambridge University Press. This book was released on 2014 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.

Large Sample Covariance Matrices and High-Dimensional Data Analysis

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Publisher : Cambridge University Press
ISBN 13 : 9781107065178
Total Pages : 0 pages
Book Rating : 4.0/5 (651 download)

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Book Synopsis Large Sample Covariance Matrices and High-Dimensional Data Analysis by : Jianfeng Yao

Download or read book Large Sample Covariance Matrices and High-Dimensional Data Analysis written by Jianfeng Yao and published by Cambridge University Press. This book was released on 2015-03-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors' own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods.

Principles of Clinical Cancer Research

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Publisher : Springer Publishing Company
ISBN 13 : 1617052396
Total Pages : 576 pages
Book Rating : 4.6/5 (17 download)

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Book Synopsis Principles of Clinical Cancer Research by : Loren K. Mell, MD

Download or read book Principles of Clinical Cancer Research written by Loren K. Mell, MD and published by Springer Publishing Company. This book was released on 2018-11-28 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Clinical Cancer Research provides comprehensive coverage of the fundamentals of clinical cancer research, including the full spectrum of methodologies used in the field. For those involved in research or considering research careers, this book offers a mix of practical advice and analytical tools for effective training in theoretical principles as well as specific, usable teaching examples. The clinical oncologist or trainee will find a high-yield, practical guide to the interpretation of the oncology literature and the application of data to real-world settings. Valuable for both researchers and clinicians who wish to sharpen their skills, this book contains all of the cornerstones and explanations needed to produce and recognize quality clinical science in oncology. Written from the physician-scientist’s perspective, the book lays a strong foundation in preclinical sciences that is highly relevant to careers in translational oncology research along with coverage of population and outcomes research and clinical trials. It brings together fundamental principles in oncology with the statistical concepts one needs to know to design and interpret studies successfully. With each chapter including perspectives of both clinicians and scientists or biostatisticians, Principles of Clinical Cancer Research provides balanced, instructive, and high-quality topic overviews and applications that are accessible and thorough for anyone in the field. KEY FEATURES: Gives real-world examples and rationales behind which research methods to use when and why Includes numerous tables featuring key statistical methods and programming commands used in everyday clinical research Contains illustrative practical examples and figures in each chapter to help the reader master concepts Provides tips and pointers for structuring a career, avoiding pitfalls, and achieving success in the field of clinical cancer research Access to fully downloadable eBook

Big Data Analytics in Oncology with R

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Publisher : CRC Press
ISBN 13 : 1000823717
Total Pages : 237 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Big Data Analytics in Oncology with R by : Atanu Bhattacharjee

Download or read book Big Data Analytics in Oncology with R written by Atanu Bhattacharjee and published by CRC Press. This book was released on 2022-12-29 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area. Features: Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.

Systems Biomedicine Approaches in Cancer Research

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Publisher : Springer Nature
ISBN 13 : 9811919534
Total Pages : 170 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Systems Biomedicine Approaches in Cancer Research by : Shailza Singh

Download or read book Systems Biomedicine Approaches in Cancer Research written by Shailza Singh and published by Springer Nature. This book was released on 2022-08-10 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the applications of systems biology and synthetic biology in cancer medicine. It highlights the use of computational and mathematical models to decipher the complexity of cancer heterogeneity. The book emphasizes the modeling approaches for predicting behavior of cancer cells, tissues in context of drug response, and angiogenesis. It introduces cell-based therapies for the treatment of various cancers and reviews the role of neural networks for drug response prediction. Further, it examines the system biology approaches for the identification of medicinal plants in cancer drug discovery. It explores the opportunities for metabolic engineering in the realm of cancer research towards development of new cancer therapies based on metabolically derived targets. Lastly, it discusses the applications of data mining techniques in cancer research. This book is an excellent guide for oncologists and researchers who are involved in the latest cancer research.

Sparse Graphical Modeling for High Dimensional Data

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Publisher : CRC Press
ISBN 13 : 0429582900
Total Pages : 150 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Sparse Graphical Modeling for High Dimensional Data by : Faming Liang

Download or read book Sparse Graphical Modeling for High Dimensional Data written by Faming Liang and published by CRC Press. This book was released on 2023-08-02 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines. Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selection Effective methods of high-dimensional inference

Feature Selection for High-Dimensional Data

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Publisher : Springer
ISBN 13 : 3319218581
Total Pages : 163 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Feature Selection for High-Dimensional Data by : Verónica Bolón-Canedo

Download or read book Feature Selection for High-Dimensional Data written by Verónica Bolón-Canedo and published by Springer. This book was released on 2015-10-05 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

Deep Sciences for Computing and Communications

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Publisher : Springer Nature
ISBN 13 : 3031689054
Total Pages : 528 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Deep Sciences for Computing and Communications by : Annie Uthra R.

Download or read book Deep Sciences for Computing and Communications written by Annie Uthra R. and published by Springer Nature. This book was released on with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Empirical Analysis of Predictive Machine Learning Algorithms on High-dimensional Microarray Cancer Data

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Publisher :
ISBN 13 :
Total Pages : 630 pages
Book Rating : 4.:/5 (919 download)

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Book Synopsis An Empirical Analysis of Predictive Machine Learning Algorithms on High-dimensional Microarray Cancer Data by : Jo A. Bill

Download or read book An Empirical Analysis of Predictive Machine Learning Algorithms on High-dimensional Microarray Cancer Data written by Jo A. Bill and published by . This book was released on 2015 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: