High Dimensional Data Analysis In Cancer Research
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High Dimensional Data Analysis In Cancer Research
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Author : Xiaochun Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-19
High Dimensional Data Analysis In Cancer Research written by Xiaochun Li and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-19 with Medical categories.
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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Author : Xiaochun Li
language : en
Publisher: Springer
Release Date : 2008-11-01
High Dimensional Data Analysis In Cancer Research written by Xiaochun Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-01 with Medical categories.
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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Author : Harris G. Fienberg
language : en
Publisher: Springer
Release Date : 2016-08-23
High Dimensional Single Cell Analysis written by Harris G. Fienberg and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Medical categories.
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 Data Analysis
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Author : Tony Cai;Xiaotong Shen
language : en
Publisher:
Release Date :
High Dimensional Data Analysis written by Tony Cai;Xiaotong Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
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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Author : Arnoldo Frigessi
language : en
Publisher: Springer
Release Date : 2016-02-16
Statistical Analysis For High Dimensional Data written by Arnoldo Frigessi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-16 with Mathematics categories.
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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Author : Inge Koch
language : en
Publisher: Cambridge University Press
Release Date : 2014
Analysis Of Multivariate And High Dimensional Data written by Inge Koch and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Business & Economics categories.
This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.
Big Data Analytics In Oncology With R
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Author : Atanu Bhattacharjee
language : en
Publisher: CRC Press
Release Date : 2022-12-29
Big Data Analytics In Oncology With R written by Atanu Bhattacharjee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-29 with Mathematics categories.
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.
Application Of Novel Statistical And Machine Learning Methods To High Dimensional Clinical Cancer And Multi Omics Data
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Author : Chao Xu
language : en
Publisher: Frontiers Media SA
Release Date : 2022-02-02
Application Of Novel Statistical And Machine Learning Methods To High Dimensional Clinical Cancer And Multi Omics Data written by Chao Xu and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Science categories.
Big And Complex Data Analysis
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Author : S. Ejaz Ahmed
language : en
Publisher: Springer
Release Date : 2017-03-21
Big And Complex Data Analysis written by S. Ejaz Ahmed and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-21 with Mathematics categories.
This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
Bayesian Approaches In Oncology Using R And Openbugs
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Author : Atanu Bhattacharjee
language : en
Publisher: CRC Press
Release Date : 2020-12-21
Bayesian Approaches In Oncology Using R And Openbugs written by Atanu Bhattacharjee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-21 with Mathematics categories.
Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters. Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS. This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework: Bayesian in Clinical Research and Sample Size Calcuation Bayesian in Time-to-Event Data Analysis Bayesian in Longitudinal Data Analysis Bayesian in Diagnostics Test Statistics This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.