Bayesian Pathway Analysis of Cancer Microarray Data

Bayesian Pathway Analysis of Cancer Microarray Data

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTB...

Pathway Analysis of Microarray Data

During the past decade remarkable new techniques for transcriptional profiling have been developed. These include transcriptional profiling using hybridization microarrays as well as methods to sequence transcribed RNAs. No matter which technology is used, these experiments generate data on thousands of genes across multiple conditions and therefore the analysis of these data is often a dauntin...

Bayesian integrated functional analysis of microarray data

MOTIVATION The statistical analysis of microarray data usually proceeds in a sequential manner, with the output of the previous step always serving as the input of the next one. However, the methods currently used in such analyses do not properly account for the fact that the intermediate results may not always be correct, then leading to cumulating error in the inferences drawn based on such s...

Microarray Data Mining for Biological Pathway Analysis

In recent years, microarray gene expression studies have been actively pursued for extracting significant biological knowledge hidden under a large volume of gene expression profiles accumulated by DNA microarray experiments. Particularly great attentions have been paid to a variety of data mining schemes for gene function discovery [Eisen et al., 1998], disease diagnosis [Saiki et al., 2008], ...

Bayesian Prediction in Spatial Data Analysis