PMC:4307189 / 12037-13288 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4307189","sourcedb":"PMC","sourceid":"4307189","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4307189","text":"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 ij=l o g(n ij) is the scaling parameter, which is fixed due to the constraint ∑kYijk=1; the hyperparameter τ θ represents precision of the normal prior for the parameter θ.","divisions":[{"label":"label","span":{"begin":0,"end":8}}],"tracks":[]}