Learning the tree of phenotypes using genomic data and VISDA

Yuanjian Feng, Zuyi Wang, Yitan Zhu, Jianhua Xuan, David J. Miller, Robert Clarke, Eric P. Hoffman, Yue Wang

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

2 Scopus citations

Abstract

Though supervised and unsupervised analyses of genomic data have been intensively studied in recent years, little effort has been made to discover the structural information contained in the data. In this work, we propose a stability analysis guided supervised clustering and visualization method aiming to discover the hierarchical structure in gene expression data, which we call the "tree of phenotypes". We applied the method on two multiclass gene expression microarray data sets and presented the biological plausibility of the learned trees. We also tested the multiclass classifiers built on the learned trees and demonstrated their good classification performance.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006
Pages165-170
Number of pages6
DOIs
StatePublished - 2006
Externally publishedYes
Event6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006 - Arlington, VA, United States
Duration: Oct 16 2006Oct 18 2006

Publication series

NameProceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006

Other

Other6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006
CountryUnited States
CityArlington, VA
Period10/16/0610/18/06

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

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