Amazon cover image
Image from Amazon.com

Statistical diagnostics for cancer : analyzing high-dimensional data / edited by Frank Emmert-Streib and Matthias Dehmer.

Contributor(s): Material type: TextTextSeries: Quantitative and network biology ; v. 3.Publisher: Weinheim, Germany : Wiley-Blackwell, [2013]Copyright date: ©2013Edition: First editionDescription: 1 online resource (xx, 292 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783527665471
  • 3527665471
  • 9783527665440
  • 3527665447
  • 9783527665457
  • 3527665455
  • 9781299158511
  • 129915851X
  • 9783527665464
  • 3527665463
Subject(s): Genre/Form: Additional physical formats: Print version:: Statistical diagnostics for cancer.DDC classification:
  • 616.99/4075 23
LOC classification:
  • RC270 .S73 2013eb
NLM classification:
  • QZ 241
Online resources:
Contents:
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.
Summary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

Edition statement from running title area.

Includes bibliographical references and index.

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.

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.

Description based on online resource; title from resource home page (ebrary, viewed October 8, 2015).

Life Sciences