A novel network control model for identifying personalized driver genes in cancer

• DOI: 10.1371/journal.pcbi.1007520
• Publication Date: 2019-11-25T13:35:42Z
• Authors: Wei-Feng Guo; Shao-Wu Zhang; Tao Zeng; Yan Li; Jianxi Gao; Luonan Chen
• Keywords: Network model; Gene regulatory network; Personalized Medicine

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized genes by using samples of an individual patient. Recently, methods exploiting structure-based control principles complex networks provide new clues for identifying minimum number nodes drive state transition large-scale from initial desired state. However, network cannot be directly applied due unknown dynamics system. Here we proposed model (PNC) employing principle on genetic data patients. In PNC model, firstly presented a paired single sample construction method construct capturing phenotype transitions between healthy and disease states. Then, designed novel Feedback Vertex Sets-based perspective genes. The wide experimental results 13 cancer datasets Cancer Genome Atlas showed that outperforms current state-of-the-art methods, in terms F-measures enriched gold-standard gene lists. Furthermore, these can explored their characteristics even when they are hidden factors transcription mutation profiles. Our gives insights useful tools into understanding tumor heterogeneity cancer. package resources used this work freely downloaded https://github.com/NWPU-903PR/PNC.