2017
DOI: 10.1109/tcbb.2015.2511758
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Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net

Abstract: 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 interac… Show more

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Cited by 24 publications
(6 citation statements)
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References 45 publications
(45 reference statements)
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“…Yamasaki et al reported that prothymosin-α (PTMA) and purine nucleoside phosphorylase (PNP) were directly regulated by miR-1-1, miR-1-2, miR-133a-1, and miR-133a-2. Silencing of these genes significantly inhibited cell proliferation, invasion and apoptosis in BC cells (33, 34). Jin et al (35) reported that miR-15a inhibited the components of TGF-β signaling pathways in LNCaP cell line, which might be related to the progression and metastasis of prostate cancer.…”
Section: Discussionmentioning
confidence: 99%
“…Yamasaki et al reported that prothymosin-α (PTMA) and purine nucleoside phosphorylase (PNP) were directly regulated by miR-1-1, miR-1-2, miR-133a-1, and miR-133a-2. Silencing of these genes significantly inhibited cell proliferation, invasion and apoptosis in BC cells (33, 34). Jin et al (35) reported that miR-15a inhibited the components of TGF-β signaling pathways in LNCaP cell line, which might be related to the progression and metastasis of prostate cancer.…”
Section: Discussionmentioning
confidence: 99%
“… 8 The development of cancer may be related to interactions between several key genes. 9 Lung cancer prognosis-associated biomarkers have been proposed on the basis of omics data, including DNA methylation, 10 gene expression, 11 microRNA, 12 and long noncoding RNA. 13 However, most studies are limited to a single type of omics data, which results in less accurate prognostic models.…”
mentioning
confidence: 99%
“…For example, under the assumption that miRNA-mRNA target pairs will have correlated expression, Gade et al built a bipartite network of mRNA and miRNA target information in order to fuse miRNA and mRNA expression profiles and guide selection of non-redundant mRNA and miRNA features [121]. A method called GIREN uses an interaction network to group features, assuming that proximity in the network indicates functional redundancy [129].…”
Section: Network Analysis Across Tumor Cohortsmentioning
confidence: 99%