A knowledge driven regression model for gene expression and microarray analysis.
The linear regression model has been widely used in the analysis of gene expression and microarray data to identify a subset of genes that are important to a given metabolic function. One of the key challenges in applying the linear regression model to gene expression data analysis arises from the sparse data problem, in which the number of genes is significantly larger than the number of conditions. To resolve this problem, we present a knowledge driven regression model that incorporates the knowledge of genes from the Gene Ontology (GO) database into the linear regression model. It is based on the assumption that two genes are likely to be assigned similar weights when they share similar sets of GO codes. Empirical studies show that the proposed knowledge driven regression model is effective in reducing the regression errors, and furthermore effective in identifying genes that are relevant to a given metabolite.
Jin
Rong
R
Faculty of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824, USA. rongjin@cse.msu.edu
Si
Luo
L
Srivastava
Shireesh
S
Li
Zheng
Z
Chan
Christina
C
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D008660:Metabolism; D046228:Microarray Analysis / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D010100:Oxygen / Q000378:metabolism; D010363:Pattern Recognition, Automated; D012044:Regression Analysis
D010100:Oxygen