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An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules
Conclusion Considering the heterogeneity and complexity of ovarian cancer, we demonstrated a new method to predict the prognosis of ovarian cancer based on the clustering information and gene co-expression network in each subtype of cancer patients. We divided the ovarian cancer data into three subtypes by clustering analysis and we found that the survival risks in these three subtypes were significantly different. We mined the important communities based on the co-expression networks in each subtype. There are 50, 73, and 92 communities in the first, second and third subtype, respectively. Next, we constructed a new ense...
Source: Frontiers in Genetics - April 23, 2019 Category: Genetics & Stem Cells Source Type: research

Ranking novel cancer driving synthetic lethal gene pairs using TCGA data.
In this study, we propose an efficient and comprehensive in-silico pipeline to rank novel SL gene pairs by mining vast amounts of accumulated tumor high-throughput sequencing data in The Cancer Genome Atlas (TCGA), coupled with other protein interaction networks and cell line information. Our pipeline integrates three significant features, including mutation coverage in TCGA, driver mutation probability and the quantified cancer network information centrality, into a ranking model for SL gene pair identification, which is presented as the first learning-based method for SL identification. As a result, 107 potential SL gene...
Source: Oncotarget - July 21, 2016 Category: Cancer & Oncology Tags: Oncotarget Source Type: research