The Role of 22 Genes Expression in Bladder Cancer by Adaptive LASSO

Shahraki, Hadi Raeisi; Jaberipoor, Mansooreh; Zare, Najaf; Hosseini, Ahmad

doi:10.17795/ijcp-5051

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The course of dimensionality leads to ill posed situation, when traditional statistical modeling is used. Traditional models are also inapplicable when the number of variables is greater than the sample size (3). To avoid the aforementioned challenges, researchers proposed penalized regression models and machine learning techniques in the last decades (4,5).…”

Section: Introductionmentioning

confidence: 99%

Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

et al. 2017

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Background: Immunoglobulin A nephropathy (IgAN) is considered a chronic renal disease and the most prevalent glomerulonephritis throughout the world. In order to model a large number of extracted biomarkers and identify the most effective biomarkers on IgAN disease, the researchers implemented 2 methods of penalized regression, known as LASSO and MCP logistic regression versus random forest method, which are appropriate for high dimensional and low sample size problems.

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Section: Introductionmentioning

confidence: 99%

Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

et al. 2017

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Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering Methods

Pourahmad

Foroozani

Nourelahi

et al. 2021

Asian Pac J Cancer Prev

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Background: Comparison of gene expression algorithms may be beneficial for obtaining disease pattern or grouping patients based on the gene expression profile. The current study aimed to investigate whether the knowledge within these data is able to group the ovarian cancer patients with similar disease pattern. Methods: Four different clustering methods were applied on 20 genes expression data of 37 women with ovarian cancer. All selected genes in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and etc. Comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were used to evaluate the performance of clustering methods. Results: Six out of 20 genes (IFN-γ, Foxp3, IL-4, BCL-2, Oct4 and survivin) selected by the Laplacian score showed key roles in the development of ovarian cancer and their prognostic values were clinically and statistically confirmed. The results indicated proper capability of the expression pattern of these genes in grouping the patients with similar prognosis, i.e. patients alive after 5 years or dead (62.12%). Conclusion: The results revealed the better performance for k-means and hierarchical clustering methods, and confirmed the fact that by using the expression profile of these genes, patients with similar behavior can be grouped in the same cluster with acceptable accuracy level. Certainly, the useful information from these data may contribute to the prediction of prognosis in ovarian cancer patients along with other features of patients.

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The Role of 22 Genes Expression in Bladder Cancer by Adaptive LASSO

Abstract: Background: Genetic expression has been frequently considered as an efficient method for early diagnosis of cancer. In this study, we examined the simultaneous effect of 22 genes on contribution to bladder cancer.

Cited by 2 publications

References 13 publications

Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering Methods

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