Integrating heterogeneous datasets for cancer module identification

AKM Azad

    Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

    Abstract

    The availability of multiple heterogeneous high-throughput datasets provides an enabling resource for cancer systems biology. Types of data include: Gene expression (GE), copy number aberration (CNA), miRNA expression, methylation, and protein-protein Interactions (PPI). One important problem that can potentially be solved using such data is to determine which of the possible pair-wise interactions among genes contributes to a range of cancer-related events, from tumorigenesis to metastasis. It has been shown by various studies that applying integrated knowledge from multi-omics datasets elucidates such complex phenomena with higher statistical significance than using a single type of dataset individually. However, computational methods for processing multiple data types simultaneously are needed. This chapter reviews some of the computational methods that use integrated approaches to find cancer-related modules.

    Original languageEnglish
    Title of host publicationMethods in Molecular Biology
    Subtitle of host publicationVolume II: Structure, Function, and Applications
    EditorsJonathan M. Keith
    PublisherHumana Press
    Pages119-137
    Number of pages19
    EditionSecond
    ISBN (Electronic)9781493966134
    ISBN (Print)9781493966110
    DOIs
    Publication statusPublished - 2017

    Publication series

    NameMethods in Molecular Biology
    PublisherHumana Press, Inc.
    Volume1526
    ISSN (Print)1064-3745
    ISSN (Electronic)1940-6029

    Keywords

    • Cancer modules
    • Cancer systems biology
    • Data integration
    • Gene-gene network
    • Multiomics dataset

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