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Texas A&M University

1. Khunlertgit, Navadon. Improvement of Reproducibility in Cancer Classification Based on Pathway Markers and Subnetwork Markers.

Degree: 2016, Texas A&M University

Identification of robust biomarkers for cancer prognosis based on gene expression data is an important research problem in translational genomics. The high-dimensional and small-sample-size data setting makes the prediction of biomarkers very challenging. Biomarkers have been identified based solely on gene expression data in the early stage. However, very few of them are jointly shared among independent studies. To overcome this irreproducibility, the integrative approach has been proposed to identify better biomarkers by overlaying gene expression data with available biological knowledge and investigating genes at the modular level. These module-based markers jointly analyze the gene expression activities of closely associated genes; for example, those that belong to a common biological pathway or genes whose protein products form a subnetwork module in a protein-protein interaction network. Several studies have shown that modular biomarkers lead to more accurate and reproducible prognostic predictions than single-gene markers and also provide the better understanding of the disease mechanisms. We propose novel methods for identifying modular markers which can be used to predict breast cancer prognosis. First, to improve identification of pathway markers, we propose using probabilistic pathway activity inference and relative expression analysis. Then, we propose a new method to identify subnetwork markers based on a message-passing clustering algorithm, and we further improve this method by incorporating topological attribute using association coefficients. Through extensive evaluations using multiple publicly available datasets, we demonstrate that all of the proposed methods can identify modular markers that are more reliable and reproducible across independent datasets compared to those identified by existing methods, hence they have the potential to become more effective prognostic cancer classifiers. Advisors/Committee Members: Yoon, Byung-Jun (advisor), Dougherty, Edward R (committee member), Pfister, Henry D (committee member), Ivanov, Ivan (committee member).

Subjects/Keywords: Cancer classification; Pathway marker; Subnetwork marker; PPI network; Subnetwork identification; Modular activity inference; Modular marker

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khunlertgit, N. (2016). Improvement of Reproducibility in Cancer Classification Based on Pathway Markers and Subnetwork Markers. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/159035

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Khunlertgit, Navadon. “Improvement of Reproducibility in Cancer Classification Based on Pathway Markers and Subnetwork Markers.” 2016. Thesis, Texas A&M University. Accessed November 19, 2019. http://hdl.handle.net/1969.1/159035.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khunlertgit, Navadon. “Improvement of Reproducibility in Cancer Classification Based on Pathway Markers and Subnetwork Markers.” 2016. Web. 19 Nov 2019.

Vancouver:

Khunlertgit N. Improvement of Reproducibility in Cancer Classification Based on Pathway Markers and Subnetwork Markers. [Internet] [Thesis]. Texas A&M University; 2016. [cited 2019 Nov 19]. Available from: http://hdl.handle.net/1969.1/159035.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khunlertgit N. Improvement of Reproducibility in Cancer Classification Based on Pathway Markers and Subnetwork Markers. [Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/159035

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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