Last edited by Faegul
Monday, July 27, 2020 | History

2 edition of High-dimensional data analysis in cancer research found in the catalog.

High-dimensional data analysis in cancer research

High-dimensional data analysis in cancer research

  • 298 Want to read
  • 1 Currently reading

Published by Springer in New York, NY .
Written in English

    Subjects:
  • Oncology,
  • Multivariate analysis

  • Edition Notes

    Includes bibliographical references and index.

    StatementXiaochun Li, Ronghui Xu, editors.
    SeriesApplied bioinformatics and biostatistics in cancer research
    ContributionsLi, Xiaochun., Xu, Ronghui, 1969-
    Classifications
    LC ClassificationsRC267 .H54 2009
    The Physical Object
    Paginationxiv, 149 p. :
    Number of Pages149
    ID Numbers
    Open LibraryOL24001561M
    ISBN 109780387697635, 9780387697659
    LC Control Number2008940562

    The LASSO penalized regression method is a popular variable selection technique used for the analysis of the high-throughput and high-dimensional data. Given high-dimensional microarray data, the LASSO method can identify the most important genes that are related to the phenotype of interest in a fast and effective : Haijun Gong, Tong Tong Wu, Edmund Clarke.   Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional bio-medical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not Cited by:

    $\begingroup$ I disagree slightly with the statement "if you have high-dimensional datasets with few data points, you're unlikely to be able to learn much from it." Time-series data is usually high-dimensional, but you can often get good insights from relatively few (dozens, hundreds) of data examples relative to the number of dimensions in each example of data (hundreds, thousands, . Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities.

    High-dimensional cytometry has made it possible to systematically measure mechanisms of tumor initiation, progression, and therapy resistance on millions of individual cells from human tumors. This has ushered in a ‘‘single-cell systems biology’’ view of cancer (‘‘High-Dimensional Single-Cell Cancer Biology’’).File Size: 6MB. Cambridge Core - Genomics, Bioinformatics and Systems Biology - Analysis of Multivariate and High-Dimensional Data - by Inge Koch This book has been cited by the following publications. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and : Inge Koch.


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High-dimensional data analysis in cancer research Download PDF EPUB FB2

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.5/5(1). High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics.

All the chapters. High-Dimensional Data Analysis in Cancer Research PDF Free Download E-BOOK DESCRIPTION Multivariate analysis is a mainstay of statistical tools in.

High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics.

All the chapters Author: Xiaochun Li. ISBN: OCLC Number: Description: xiv, pages: illustrations: Contents: On the Role and Potential of High-Dimensional Biologic Data in Cancer Research Author by: Arnoldo Frigessi Languange: en Publisher by: Springer Format Available: PDF, ePub, Mobi Total Read: 99 Total Download: File Size: 54,5 Mb Description: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May The focus of the.

High-dimensional Microarray Data Analysis: Cancer Gene Diagnosis and Malignancy Indexes by Microarray Book March with 44 Reads How we measure 'reads'. Request PDF | Release from the Curse of High Dimensional Data Analysis | Golub et al.

started their research to find oncogenes and new cancer subclasses from. Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data (Quantitative and Network Biology (VCH) Book 3) - Kindle edition by Dehmer, Matthias.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data (Quantitative Manufacturer: Wiley-Blackwell.

High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics.

Download Best Book Read High-Dimensional Data Analysis in Cancer Research (Applied Bioinformatics and Biostatistics PDF Online, Download Online Read High-Dimensional Data Analysis in Cancer Research (Applied Bioinformatics and Biostatistics PDF Online Book, Download pdf Read High-Dimensional Data Analysis in Cancer Research (Applied.

It is structured around topics on multiple hypothesis testing, feature selection, regression, classification, dimension reduction, as well as applications in survival analysis and biomedical research.

The book will appeal to graduate students and new researchers interested in the plethora of opportunities available in high-dimensional data. His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory.

In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry. Sell High-Dimensional Data Analysis in Cancer Research (Applied Bioinformatics and Biostatistics in Cancer Research) - ISBN - Ship for free.

- Bookbyte. High-Dimensional Data Analysis in Cancer Research | 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.

His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry.

The Cancer Genome Atlas (TCGA) catalyzed considerable growth and advancement in the computational biology field by supporting the development of high-throughput genomic characterization technologies, generating a massive quantity of data, and fielding teams of researchers to analyze the data.

Below is a collection of some of the tools developed. 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 Brand: Xiaochun Li; Ronghui Xu. Cancer is a widely-recognized public health burden and a complex disease.

High-dimensional “Omics” research – research that uses high dimensional genomic, pro-teomic, and/or metabolomic data – has often been motivated by cancer applications ant lessons have been learned in processing and analyzing such : Kellie J.

Archer, Kevin Dobbin, Swati Biswas, Roger S. Day, David C. Wheeler, Hao Wu. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features: A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis.

'This book deals with the analysis of covariance matrices under two different assumptions: large-sample theory and high-dimensional-data theory. While the former approach is the classical framework to derive asymptotics, nevertheless the latter has received increasing attention due to its applications in the emerging field of by: high-dimensional data from single-cell assays.

Bioconductor has developed state-of-the-art and widely used software packages (Table S1) for the analysis of high-dimensional bulk assays, such as RNA-sequencing (RNA-seq) and high-throughput, low-dimensional single-cell assays, such as flow cytometry and mass cytometry (CyTOF) data.

Zhang H.H. () Support Vector Machine Classification for High Dimensional Microarray Data Analysis, With Applications in Cancer Research. In: Li X., Xu R.

(eds) High-Dimensional Data Analysis in Cancer Research. Applied Bioinformatics and Biostatistics in Cancer Research. Springer, New York, NY.

First Online 28 November Cited by: 1.