ArticleScientific reports2015
Highly informative marker sets consisting of genes with low individual degree of differential expression.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Impact of IGFBP6 Knockdown on Cholesterol Metabolism in Breast Cancer Cells.Current medicinal chemistry · 2026Article
- ELOVL5 Regulates Ferroptosis in Breast Cancer Cells.Doklady. Biochemistry and biophysics · 2025Article
- IGFBP6 Modulates Proteostasis by Activating ATF4 Targets and Reducing ER Retrotranslocon Expression.Doklady. Biochemistry and biophysics · 2024Article
- Lysine lactylation (Kla) might be a novel therapeutic target for breast cancer.BMC medical genomics · 2023Article
- Article
- ExhauFS: exhaustive search-based feature selection for classification and survival regression.PeerJ · 2022Article
- Effect of the Expression ofFrontiers in genetics · 2021Article
- miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence.PloS one · 2021Article
- Cumulative prognostic power of laminin genes in colorectal cancer.BMC medical genomics · 2018Article
- Comprehensive network of miRNA-induced intergenic interactions and a biological role of its core in cancer.Scientific reports · 2018Article
- Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma.Scientific reports · 2016Article
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Authors and funding
8 authors.
Funding
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Abstract
Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization of the cumulative informative power and identified sets of 2-5 genes efficiently predicting recurrence for ER-positive breast cancer patients. The gene combinations constructed on the basis of microarray data were successfully applied to data acquired by RNA-seq. The developed method provides the basis for the generation of highly efficient prognostic and predictive gene signatures for cancer and other diseases. The identified gene sets can potentially reveal novel essential segments of gene interaction networks and pathways implied in cancer progression.
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