Learning a highly resolved tree of phenotypes using genomic data clustering

Yuanjian Feng, David J. Miller, Robert Clarke, Eric P. Hoffman, Yue Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    A highly resolved tree of phenotypes (TOP) derived from genomic data reveals important relationships between heterogeneous diseases at molecular level. We propose a stability analysis guided learning method that produces a reproducible yet non-binary TOP using high-dimensional finite sample size genomic data. Experimental results show the superior capability of the proposed method in learning TOP with balanced stability and descriptiveness, as compared to conventional tree learning schemes.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
    Number of pages1
    DOIs
    Publication statusPublished - Dec 1 2009
    Event2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 - Washington, DC, United States
    Duration: Nov 1 2009Nov 4 2009

    Publication series

    NameProceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

    Other

    Other2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
    CountryUnited States
    CityWashington, DC
    Period11/1/0911/4/09

      Fingerprint

    All Science Journal Classification (ASJC) codes

    • Biomedical Engineering
    • Health Informatics
    • Health Information Management

    Cite this

    Feng, Y., Miller, D. J., Clarke, R., Hoffman, E. P., & Wang, Y. (2009). Learning a highly resolved tree of phenotypes using genomic data clustering. In Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 [5332074] (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009). https://doi.org/10.1109/BIBMW.2009.5332074