In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

摘要: Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable signatures of specific phenotype or reliable disease biomarkers. In the present study, we introduce in silico Pathway Activation Network Decomposition Analysis (iPANDA) as scalable robust method biomarker identification using gene expression The iPANDA combines precalculated coexpression data with importance factors based on degree differential topology decomposition obtaining scores. Using Microarray Quality Control (MAQC) sets pretreatment Taxol-based neoadjuvant breast cancer therapy multiple sources, demonstrate that provides significant noise reduction identifies highly signatures. We successfully apply stratifying patients according their sensitivity therapy. aids interpretation data, but existing algorithms fall short providing identification. introduced here includes estimation robustly identify pathways biomarkers patient stratification.