Directors Tell the Story

Rooney, Bethany; Belli, Mary Lou

doi:10.4324/9781315667812

Cited by 2 publications

(1 citation statement)

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“…The methylation percentages all underwent a reverse logistic transform such that they span the whole range of [−∞, +∞] Loci with missing values are removed from further analysis. Finally, the non-differentially expressed genes were eliminated with a two-sample t-test [33] (fold change<2, p -value<0.05). The non-differentially methylated locus were also removed by Mann-Whitney U-test [34] (fold change<1.23, p -value< 0.05).…”

Section: Tests On Real Datamentioning

confidence: 99%

Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net

Zhang

Liu

Huang

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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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-omic 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 superPC, 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.

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Section: Tests On Real Datamentioning

confidence: 99%