Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net

Lin Zhang, Hui Liu, Yufei Huang, Xuesong Wang, Yidong Chen, Jia Meng

    Research output: Contribution to journalArticlepeer-review

    22 Scopus citations


    Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omics data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity, and the 'grouping effect' in solution to select the discriminate features with prognostic value. An iterative gradient descent algorithm is also developed to solve the model with regularized optimization. GIREN was applied to human ovarian cancer and breast cancer datasets obtained from The Cancer Genome Atlas, respectively. Result shows that, the proposed GIREN algorithm obtained more accurate and robust performance over competing algorithms (LASSO, Elastic Net, and Semi-supervised PCA, with or without average pathway expression features) in predicting cancer progression on both two datasets in terms of median area under curve (AUC) and interquartile range (IQR), suggesting a promising direction for more effective integration of gene measurement and gene interaction information.

    Original languageEnglish (US)
    Pages (from-to)145-154
    Number of pages10
    JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
    Issue number1
    StatePublished - Jan 1 2017


    • DNA methylation
    • Elastic net
    • classification
    • gene expression
    • gene-gene interaction
    • microarray
    • protein-protein interaction
    • survival prediction

    ASJC Scopus subject areas

    • Applied Mathematics
    • Genetics
    • Biotechnology


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