Learning a highly resolved tree of phenotypes using genomic data clustering

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.