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

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).