Discriminant and Network Analysis to Study Origin of Cancer

Li Chen, Ye Tian, Guoqiang Yu, David Jonathan Miller, Ie Ming Shih, Yue Wang

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Enabled by rapid advances in biological data acquisition technologies and developments in computational methodologies, interdisciplinary research in machine learning for biomedicine tackles various challenging biological questions by comprehensively scrutinizing (multiplatform) data from multiple, distinct vantages. Understanding the origin and progression of cancer has great practical import for advancing both biological knowledge and potential clinical treatments. Technically, the most challenging biological questions inspire and promote the development and applications of novel computational methods. This chapter presents a coalition of state-of-the-art machine learning methods and leading-edge scientific puzzles. With DNA copy number and transcriptome data, we were able to design specific statistical hypothesis tests to reveal the origin of cancer by comparing the genomic and transcriptome codes and biological network structures.

    Original languageEnglish (US)
    Title of host publicationStatistical Diagnostics for Cancer
    Subtitle of host publicationAnalyzing High-Dimensional Data
    PublisherWiley-VCH
    Pages193-214
    Number of pages22
    Volume3
    ISBN (Print)9783527332625
    DOIs
    StatePublished - Apr 8 2013

    All Science Journal Classification (ASJC) codes

    • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Chen, L., Tian, Y., Yu, G., Miller, D. J., Shih, I. M., & Wang, Y. (2013). Discriminant and Network Analysis to Study Origin of Cancer. In Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data (Vol. 3, pp. 193-214). Wiley-VCH. https://doi.org/10.1002/9783527665471.ch11