EXEMPLARY ACHIEVEMENTS
Xinan (Holly) Yang, Ph.D.
#1
| Reference: Yang X, Regan K, Huang Y, Zhang Q, Li J, Seiwert TY, Cohen EW, Xing HR, Lussier YA. Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer. PLoS Computational Biology. (2012) 8(1):e1002350 linkage |
| Major finding: We developed a novel approach, FAIME, to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays that significantly improves the reproducibility across cohorts (46% pathway-signature overlap versus 4% gene-signature overlap). FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets, thus is more amenable for clinical deployment. |
| Reference: Yang X, Huang Y, Chen JL, Xie J, Sun X, Lussier YA. Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia. BMC Bioinformatics (2009). 10(S9):S6 linkage |
| Major finding: Based on my previously non-parametric rank-based algorithm, we proposed a novel computational strategy, PGnet, based on genes associated to known biological mechanisms to derive mechanism-anchored expression profiles that can accurately predict disease outcome. Notable, a PGnet predicted association between up-regulation of HDAC9 and childhood acute lymphoblastic leukemia relapse was validated in vivo by independent researchers (Br J Haematol, 2010, 150, 665-673). |
#3
| Reference: Yang X*(corresponding author) and Sun X. Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers. BMC Bioinformatics (2007). 6; 8(1):118. linkage |
| Major finding: We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles. |
#4
| Reference: Yang X, Bentink S, Scheid S, Spang R. Similarities of ordered gene lists. J Bioinform Comput Biol. (2006). 4(3):693-708 |
| Major finding: This novel method can uncover consistent gene signatures, a particularly important breakthrough as cancer gene signatures are often weak and lack overlap. By allowing researchers to simultaneously analyze multiple studies using this method, it was a crucial breakthrough for finding clinically promising cancer prognosis gene markers. This method has been commended as among several “sophisticated statistical measures to quantify similarity of any two microarray studies” and has been widely applied for biological discoveries. |
Copyright © 2014 by Wanqi Zhu