Krasnitz Cancer Bioinformatics

Erscheinungsjahr 2018
ISBN: 978-1-4939-8868-6
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1878, 280 Seiten, eBook

Reihe: Methods in Molecular Biology

This volume covers a wide variety of state of the art cancer-related methods and tools for data analysis and interpretation. Chapters were designed to attract a broad readership, ranging from active researchers in computational biology and bioinformatics developers, clinical oncologists, and anti-cancer drug developers wishing to rationalize their search for new compounds. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, installation instructions for computational tools discussed, explanations of the input and output formats, and illustrative examples of applications.

Authoritative and cutting-edge, Cancer Bioinformatics: Methods and Protocols aims to support researchers performing computational analysis of cancer-related data.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Krasnitz, Alexander

Inhaltsverzeichnis

An Access Primer to Repositories of Cancer-related Genomic Big Data.- Building Portable and Reproducible Cancer Informatics Workflows: An RNA Sequencing Case Study.- Computational Analysis of Structural Variation in Cancer Genomes.- CORE: A Software Tool for Delineating Regions of Recurrent DNA Copy Number Alteration in Cancer.- Identification of Mutated Cancer Driver Genes on Unpaired RNA-Seq Samples.- A Computational Protocol for Detecting Somatic Mutations by Integrating DNA and RNA Sequencing.- Allele-specific Expression Analysis in Cancer Using Next Generation Sequencing Data.- Computational Analysis of lncRNA Function in Cancer.- Computational Methods for Identification of T Cell Neoepitopes in Tumors.- Computational and Statistical Analysis of Array-based DNA Methylation Data.- Computational Methods for Subtyping Of Tumors and their Applications for Deciphering Tumor Heterogeneity.- Statistically Supported Identification of Tumor Subtypes.- Computational Methods for Analysisof Tumor Clonality and Evolutionary History.- Predictive Modeling of Anti-cancer Drug Sensitivity from Genetic Characterizations.- In silico Oncology Drug Repositioning and Polypharmacology.- Modelling Growth of Tumours and their Spreading Behaviour using Mathematical Functions.