Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Computational Biology
Group Additive Regression Models For Genomic Data Analysis, Yihui Luan, Hongzhe Li
Group Additive Regression Models For Genomic Data Analysis, Yihui Luan, Hongzhe Li
UPenn Biostatistics Working Papers
One important problem in genomic research is to identify genomic features such as gene expression data or DNA single nucleotide polymorphisms (SNPs) that are related to clinical phenotypes. Often these genomic data can be naturally divided into biologically meaningful groups such as genes belonging to the same pathways or SNPs within genes. In this paper, we propose group additive regression models and a group gradient descent boosting procedure for identifying groups of genomic features that are related to clinical phenotypes. Our simulation results show that by dividing the variables into appropriate groups, we can obtain better identification of the group …
Nonparametric Pathway-Based Regression Models For Analysis Of Genomic Data, Zhi Wei, Hongzhe Li
Nonparametric Pathway-Based Regression Models For Analysis Of Genomic Data, Zhi Wei, Hongzhe Li
UPenn Biostatistics Working Papers
High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression(NPR) analysis to …