title: A Markov random field model for network-based analysis of genomic data.
abstract: BACKGROUND
  A central problem in genomic research is the identification of genes and pathways involved in diseases and other biological processes. The genes identified or the univariate test statistics are often linked to known biological pathways through gene set enrichment analysis in order to identify the pathways involved. However, most of the procedures for identifying differentially expressed (DE) genes do not utilize the known pathway information in the phase of identifying such genes. In this article, we develop a Markov random field (MRF)-based method for identifying genes and subnetworks that are related to diseases. Such a procedure models the dependency of the DE patterns of genes on the networks using a local discrete MRF model.
  
  
  RESULTS
  Simulation studies indicated that the method is quite effective in identifying genes and subnetworks that are related to disease and has higher sensitivity and lower false discovery rates than the commonly used procedures that do not use the pathway structure information. Applications to two breast cancer microarray gene expression datasets identified several subnetworks on several of the KEGG transcriptional pathways that are related to breast cancer recurrence or survival due to breast cancer.
  
  
  CONCLUSIONS
  The proposed MRF-based model efficiently utilizes the known pathway structures in identifying the DE genes and the subnetworks that might be related to phenotype. As more biological networks are identified and documented in databases, the proposed method should find more applications in identifying the subnetworks that are related to diseases and other biological processes.
authors.affiliation: Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wei
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology / Q000601:surgery; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D005786:Gene Expression Regulation; D015894:Genome, Human; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D009362:Neoplasm Metastasis; D009364:Neoplasm Recurrence, Local; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D014158:Transcription, Genetic