Figure 1

Schematic diagram of our proposed framework. (A) The framework for finding putative drug-resistant cross-talks. At first two gene expression data matrices were generated individually from the samples of both parental and resistant conditions. Next, based on pair-wise correlations of genes’ expression values, two gene-gene relationship networks were derived. Then, a Bayesian statistical model called the p ₁-model was applied on those two networks to find posterior probabilities of network edges. These posterior probabilities were used to find gene-pairs potentially contributing to drug resistance. Next, these gene-pairs were analyzed for overlap with cross-talks between EGFR/ErbB and other signaling pathways, and thus putative drug-resistant cross-talks were identified. (B) Hierarchical Bayesian model for inferring posterior probabilities of network parameters. Here, α represents the propensity (expansiveness/attractiveness) of a gene to be connected in an undirected network, and is dependent on the hyperparameter Σ; θ is the global density parameter; λ _{i j} =l o g(n _ij ) is the scaling parameter, which is fixed due to the constraint \( \sum _{k }^{}{{Y}_{\textit {ijk}}=1} \); the hyperparameter τ _θ represents precision of the normal prior for the parameter θ.