Pathway analysis of high-throughput biological data within a Bayesian network framework
Yükleniyor...
Dosyalar
Tarih
2011-06-15
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Oxford Univ Press
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Motivation: Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and nonlinear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Results: Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC.
Açıklama
Anahtar Kelimeler
GENE-EXPRESSION DATA, RENAL-CELL CARCINOMA, FUNCTIONAL-GROUPS, SET ANALYSIS, GLOBAL TEST, KNOWLEDGE, GENOME, MODEL
Kaynak
Bioinformatics
WoS Q Değeri
Q1
