  Recent breakthroughs in cancer research have come via the up-and-coming field
of pathway analysis. By applying statistical methods to prior known gene and
protein regulatory information, pathway analysis provides a meaningful way to
interpret genomic data. While many gene/protein regulatory relationships have
been studied, never before has such a significant amount data been made
available in organized forms of gene/protein regulatory networks and pathways.
However, pathway analysis research is still in its infancy, especially when
applying it to solve practical problems.
  In this paper we propose a new method of studying biological pathways, one
that cross analyzes mutation information, transcriptome and proteomics data.
Using this outcome, we identify routes of aberrant pathways potentially
responsible for the etiology of disease. Each pathway route is encoded as a
bayesian network which is initialized with a sequence of conditional
probabilities specifically designed to encode directionality of regulatory
relationships encoded in the pathways. Far more complex interactions, such as
phosphorylation and methylation, among others, in the pathways can be modeled
using this approach. The effectiveness of our model is demonstrated through its
ability to distinguish real pathways from decoys on TCGA mRNA-seq, mutation,
Copy Number Variation and phosphorylation data for both Breast cancer and
Ovarian cancer study. The majority of pathways distinguished can be confirmed
by biological literature. Moreover, the proportion of correctly indentified
pathways is \% higher than previous work where only mRNA-seq mutation data is
incorporated for breast cancer patients. Consequently, such an in-depth pathway
analysis incorporating more diverse data can give rise to the accuracy of
perturbed pathway detection.
