TY - GEN
T1 - Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning
AU - Kawano, Shuichi
AU - Shimamura, Teppei
AU - Niida, Atsushi
AU - Imoto, Seiya
AU - Yamaguchi, Rui
AU - Nagasaki, Masao
AU - Yoshida, Ryo
AU - Print, Cristin
AU - Miyano, Satoru
PY - 2010
Y1 - 2010
N2 - 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. 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.
AB - 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. 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.
KW - Cancer heterogeneity
KW - Gene network
KW - Microarray
KW - Pathway activity
KW - Sparse supervised learning
UR - http://www.scopus.com/inward/record.url?scp=79952389487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952389487&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2010.5706572
DO - 10.1109/BIBM.2010.5706572
M3 - Conference contribution
AN - SCOPUS:79952389487
SN - 9781424483075
T3 - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
SP - 253
EP - 258
BT - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Y2 - 18 December 2010 through 21 December 2010
ER -