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
Pages355
Number of pages1
DOIs
StatePublished - 2009
Externally publishedYes
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

Conference

Conference2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
CountryUnited States
CityWashington, DC
Period11/1/0911/4/09

Fingerprint Dive into the research topics of 'Learning a highly resolved tree of phenotypes using genomic data clustering'. Together they form a unique fingerprint.

Cite this