Network-guided prediction of aromatase inhibitor response in breast cancer
Fig 1
(a) Flowchart of our general classification approach, showing the network smoothing procedure applied to multiple data types: somatic mutations, differentially expressed genes, and protein targets for a particular drug. Smoothed mutations, differential expression, and drug targets are combined into network proximity measures by computing the element-wise minimum of the smoothed scores. Correlation is computed between LINCS expression profiles and tumor gene expression measurements. UPMC and TCGA samples are handled identically for most of the analysis pipeline until performing cross-validation: UPMC samples are used both in isolation and in combination with TCGA samples. (b) shows feature availability for data types used in this analysis.