Learning a highly resolved tree of phenotypes using genomic data clustering

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

doi:10.1109/BIBMW.2009.5332074

Learning a highly resolved tree of phenotypes using genomic data clustering

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

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Number of pages	1
DOIs	https://doi.org/10.1109/BIBMW.2009.5332074
State	Published - Dec 1 2009
Event	2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 - Washington, DC, United States Duration: Nov 1 2009 → Nov 4 2009

Publication series

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

Other

Other	2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Country/Territory	United States
City	Washington, DC
Period	11/1/09 → 11/4/09

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.1109/BIBMW.2009.5332074

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 Article 5332074 (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009). https://doi.org/10.1109/BIBMW.2009.5332074

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title = "Learning a highly resolved tree of phenotypes using genomic data clustering",

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.",

author = "Yuanjian Feng and Miller, {David J.} and Robert Clarke and Hoffman, {Eric P.} and Yue Wang",

year = "2009",

month = dec,

day = "1",

doi = "10.1109/BIBMW.2009.5332074",

language = "English (US)",

isbn = "9781424451210",

series = "Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009",

booktitle = "Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009",

note = "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 ; Conference date: 01-11-2009 Through 04-11-2009",

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Feng, Y, Miller, DJ, Clarke, R, Hoffman, EP & 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, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009, Washington, DC, United States, 11/1/09. https://doi.org/10.1109/BIBMW.2009.5332074

Learning a highly resolved tree of phenotypes using genomic data clustering. / Feng, Yuanjian; Miller, David J.; Clarke, Robert et al.
Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009. 2009. 5332074 (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Clarke, Robert

AU - Hoffman, Eric P.

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AB - 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.

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T3 - Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

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

T2 - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

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Feng Y, Miller DJ, Clarke R, Hoffman EP, Wang Y. Learning a highly resolved tree of phenotypes using genomic data clustering. In Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009. 2009. 5332074. (Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009). doi: 10.1109/BIBMW.2009.5332074

Learning a highly resolved tree of phenotypes using genomic data clustering

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All Science Journal Classification (ASJC) codes

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