Title :
Learning a highly resolved tree of phenotypes using genomic data clustering
Author :
Feng, Yuanjian ; Miller, David J. ; Clarke, Robert ; Hoffman, Eric P. ; Wang, Yue
Author_Institution :
Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
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.
Keywords :
bioinformatics; diseases; genomics; learning (artificial intelligence); pattern clustering; tree data structures; genomic data clustering; heterogeneous diseases; phenotypes tree; stability analysis guided learning method; tree learning schemes; Bioinformatics; Cancer; Diseases; Gaussian noise; Gene expression; Genomics; Learning systems; Pediatrics; Stability analysis; Tree data structures;
Conference_Titel :
Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009. IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-5121-0
DOI :
10.1109/BIBMW.2009.5332074
