Title
Identifying gene pathways associated with cancer characteristics via sparse statistical methods.
Abstract
We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.
Year
DOI
Venue
2012
10.1109/TCBB.2012.48
IEEE/ACM Trans. Comput. Biology Bioinform.
Keywords
Field
DocType
cancer characteristics,cancer heterogeneity,microarray gene expression data,breast cancer gene expression,identifying gene pathways associated,cancer phenotypes,sparse statistical methods,reverse-engineer gene network,model selection criterion,model estimation,cancer phenotype,gene pathway,cancer,gene network,regression analysis,breast cancer,data mining,model selection,vectors,logistic regression model,gene expression,bioinformatics,biomedical engineering,reverse engineering,erbium,logistics,principal component analysis,microarray,supervised learning,elastic net,genetics,parameter estimation
Data mining,Regression analysis,Computer science,Elastic net regularization,Model selection,Supervised learning,Bioinformatics,Gene regulatory network,Logistic regression,Cancer,Principal component analysis
Journal
Volume
Issue
ISSN
9
4
1557-9964
Citations 
PageRank 
References 
4
0.49
4
Authors
9
Name
Order
Citations
PageRank
Shuichi Kawano1133.72
Teppei Shimamura2388.80
Atsushi Niida3605.95
Seiya Imoto497584.16
Rui Yamaguchi518026.49
Masao Nagasaki636826.22
Ryo Yoshida711911.64
Cristin Print8361.93
Satoru Miyano92406250.71