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2 edition of Analyzing high-dimensional microarray data using Variational-SOM. found in the catalog.

Analyzing high-dimensional microarray data using Variational-SOM.

Linghai Zhang

Analyzing high-dimensional microarray data using Variational-SOM.

by Linghai Zhang

  • 116 Want to read
  • 30 Currently reading

Published .
Written in English


About the Edition

This thesis focuses on analyzing high-dimensional microarray data using the proposed algorithm, Variational-SOM. The original Self-Organizing Map (SOM) algorithm is an unsupervised neural network method and can be used to reduce the dimensionality of microarray data. The main disadvantage of SOM is that the topology of the map must be fixed from the beginning. In order to solve the problem, the Variational-SOM, of which the map"s topology is determined dynamically, is proposed.The DNA microarray technology makes it possible to monitor expression levels of thousands of genes simultaneously. However, these data are of little use unless we are able to analyze them.Experimental results show that the Variational-SOM can reduce the dimensionality of data according to the information that the data contains and help to extract biological significance from the data. The analysis using Variational-SOM can produce more well-separated clusters with respect to clinical information than using the original SOM.

The Physical Object
Pagination65 leaves.
Number of Pages65
ID Numbers
Open LibraryOL20238138M
ISBN 100494021829

Example data sets generated using catalog GeneChip™, Axiom™, Cytoscan™, OncoScan™, DMET, and Clariom™ arrays Microarray Comparison Analysis Spreadsheets Comparison analysis spreadsheets are designed to help expression analysis customers compare and understand the relationship between the data generated using related GeneChip arrays. Background: Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and usly, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data.

This innovative book includes in-depth presentations of genomic signal processing, artificial neural network use for microarray data analysis, signal processing and design of microarray time series experiments, application of regression methods, gene expression profiles and prognostic markers for primary breast cancer, and factors affecting the. *Shi P, Zhang A and Li H (): Regression Analysis for Microbiome Compositional of Applied Statistics, 10(2): Cai T, Li H, Liu W and Xie J (): Joint Estimation of Multiple High-dimensional Precision tica Sinica, 26(2), Chen EZ and Li H (): A two-part mixed-effect model for analyzing longitudinal microbiome compositional data.

The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and. The currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex. In this paper, we proposed to use a revised locally linear embedding(LLE) method, which is purely unsupervised and fast as the feature extraction strategy for microarray data analysis.


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Analyzing high-dimensional microarray data using Variational-SOM by Linghai Zhang Download PDF EPUB FB2

The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on.

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. However, the major problem that persists in the analysis of microarray is the high dimension of the data.

This high dimensional dataset increases computational complexity. Hence, it becomes essential to scale down the dimension of the feature vector for the proper analysis of cancer microarray : Santos Kumar Baliarsingh, Swati Vipsita, Amir H. Gandomi, Abhijeet Panda, Sambit Bakshi, Somula Rama.

HIGH-DIMENSIONAL MICROARRAY DATA ANALYSIS - CANCER GENE DIAGNOSIS AND MALIGNANCY INDEXES BY MICROARRAY - | We can firstly succeed in cancer gene diagnosis and propose the cancer gene diagnosis. Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment.

[citation needed] Such experiments can generate very large amounts of data. 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 Analyzing high-dimensional microarray data using Variational-SOM.

book, 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). Microarray Data Analysis is called expression ratio.

It is denoted here as Tk and defi ned as: and defi ned as: k Tk = Rk G For each gene k on the array, where on the array, where Rk represents the spot intensity metric for the test sample and Gk represents the spot intensity metric for the reference mentioned.

Objectives of Microarray Studies. Effective microarray experiments require careful planning based on clear objectives[].The objective drives the selection of specimens and the specification of an appropriate analysis strategy [].The large numbers of genes whose expressions can be measured in a single hybridization creates an even greater than usual need for careful.

Introduction. The use of gene expression profiling has increased dramatically but serious problems in the analysis of such data in publications are prevalent (Dupuy and Simon, ; Michiels et al.

).Valid analysis of DNA microarray experiments requires substantial statistical knowledge but statisticians with expertise in microarray methods are in short supply. Downloadable. Background: Critical to the development of molecular signatures from microarray and other high-throughput data is testing the statistical significance of the produced signature in order to ensure its statistical reproducibility.

While current best practices emphasize sufficiently powered univariate tests of differential expression, little is known about the factors that affect. Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-dimensional microarray data classification, due to the small number of samples and large number of features, classical LDA has sub-optimal performance corresponding to the singularity and instability of the within-group covariance matrix.

Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition Book September with 23 Reads How we measure 'reads'. Key words: High-dimensional data mining, frequent pattern, clustering high-dimensional data, classifying high-dimensional data 1.

INTRODUCTION The emergence of various new application domains, such as bioinformatics and e-commerce, underscores the need for analyzing high dimensional data. In a gene expression microarray data set, there could be.

• Smyth GK et al. Statistical issues in cDNA microarray data analysis. Methods Mol Biol. • Pavlidis P. Using ANOVA for gene selection from microarray studies of the nervous system. Methods.

31(4), • Quackenbush J. Computational analysis of microarray data. Nature Reviews Genetics   Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data 6 April | Bioinformatics, Vol. 21, No. 13 Constructing and Analyzing a Large-Scale Gene-to-Gene Regulatory Network-Lasso-Constrained Inference and Biological Validation.

McLachlan's excellent book needs to read before one embarks on analysis of microarray data. The book lays the foundation of the statistical methods relevant for analysis of microarray data.

An applied analyst/practitioner can use the book by Deshmukh and s: 2. All analysis done using the gbm package in R (Greg Ridgeway). Leathwick, D. Rowe, J. Richardson, J. Elith and T.

Hastie, Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshwater Biology 50 Presence-absence species data are modelled using a MARS along with.

This research focuses on dynamic analysis of reduced dimension models of two microarray time series data sets. Underlying research achieves two main objectives; namely, (1) various dimension reduction techniques used on time series microarray data, and (2) estimating auto regressive coefficients using several penalized regression methods like ridge, SCAD, and.

Microarray analysis exercises 1 - with R WIBR Microarray Analysis Course - Starting Data (probe data) Starting Data (summarized probe data): [] [] [] [] Processed Data (starting with MAS5) Introduction. You'll be using a sample of expression data from a study using Affymetrix (one color) U95A arrays that were hybridized to tissues from fetal and human liver and brain.

Get this from a library. High-dimensional microarray data analysis: cancer gene diagnosis and malignancy indexes by microarray.

[Shuichi Shinmura] -- 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.

The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical and computational problems, largely driven by the complex interrelationships between these high-dimensional genomic measurements.This book, instead, is a reference that delves deeply, DEEPLY into the statistics that underlie the analysis of microarray-type experiments.

(I.e., experiments with thousands of measurements that may or may not have some level of correlation), and how to identify correlative data, how to find patterns, and how to find things that are the same.Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis.

A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used.