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Detection of Significantly Perturbed Subnetworks in Cancer.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Detection of Significantly Perturbed Subnetworks in Cancer./
作者:	Yang, Le.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	112 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28089740
ISBN:	9798672196558

Detection of Significantly Perturbed Subnetworks in Cancer.
Yang, Le.

Detection of Significantly Perturbed Subnetworks in Cancer. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 112 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2020.

This item must not be sold to any third party vendors.

Identifying cancer driver genes and pathways is a central task of cancer research. The mainstay gene-level analysis does not provide sufficient statistical power to detect rarely mutated but functionally important genes and typically results in a list of putative driver genes without a unified theme of biological processes. A promising approach to overcoming the above issues is to perform de novo pathway analysis by overlaying mutation data onto a protein-protein interaction (PPI) network and detecting functional modules that are significantly disrupted in cancer. With the accumulation of protein interaction information, pathway analysis has become a major research topic in systems biology. Computationally, detecting cancer pathways is essentially a combinatorial optimization problem, which is much more difficult than statistical tests of mutational abundance. Although significant efforts have been made in the past two decades, several key issues are outstanding, including those related to detect functionally homogenous subnetworks in a scale-free biological network, to control the false discovery rates (FDRs) of identified subnetworks, to achieve provably optimal solutions, to integrate genetic data from multiple platforms, and to solve the computational complexity.This dissertation proposes a novel algorithm, referred to as FDRnet, to address the aforementioned longstanding issues that have challenged the research community in de novo pathway analysis. To overcome the hurdle of assessing the statistical significance of detected subnetworks, we propose a novel FDR definition for subnetwork identification, which also facilitates the integration of multi-omics data. By using the new definition, we formulate the subnetwork identification problem as a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint and minimizing conductance scores to find dense subgraphs around seed genes. To address computational issues, several novel algorithmic strategies are also developed. A large-scale benchmark study was performed on both simulation and cancer data. FDRnet significantly outperformed existing approaches in terms of computational efficiency and the ability to detect functionally homogenous subnetworks in a scale-free biological network, to control FDRs of detected subnetworks, to provide provably optimal solutions, and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases. With the accumulation of gene- interaction information and genomic data, we believe that our method can find wide applications in human disease research.

ISBN: 9798672196558Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Cancer

Detection of Significantly Perturbed Subnetworks in Cancer.
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300 $a 112 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
500 $a Advisor: Sun, Yijun.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Identifying cancer driver genes and pathways is a central task of cancer research. The mainstay gene-level analysis does not provide sufficient statistical power to detect rarely mutated but functionally important genes and typically results in a list of putative driver genes without a unified theme of biological processes. A promising approach to overcoming the above issues is to perform de novo pathway analysis by overlaying mutation data onto a protein-protein interaction (PPI) network and detecting functional modules that are significantly disrupted in cancer. With the accumulation of protein interaction information, pathway analysis has become a major research topic in systems biology. Computationally, detecting cancer pathways is essentially a combinatorial optimization problem, which is much more difficult than statistical tests of mutational abundance. Although significant efforts have been made in the past two decades, several key issues are outstanding, including those related to detect functionally homogenous subnetworks in a scale-free biological network, to control the false discovery rates (FDRs) of identified subnetworks, to achieve provably optimal solutions, to integrate genetic data from multiple platforms, and to solve the computational complexity.This dissertation proposes a novel algorithm, referred to as FDRnet, to address the aforementioned longstanding issues that have challenged the research community in de novo pathway analysis. To overcome the hurdle of assessing the statistical significance of detected subnetworks, we propose a novel FDR definition for subnetwork identification, which also facilitates the integration of multi-omics data. By using the new definition, we formulate the subnetwork identification problem as a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint and minimizing conductance scores to find dense subgraphs around seed genes. To address computational issues, several novel algorithmic strategies are also developed. A large-scale benchmark study was performed on both simulation and cancer data. FDRnet significantly outperformed existing approaches in terms of computational efficiency and the ability to detect functionally homogenous subnetworks in a scale-free biological network, to control FDRs of detected subnetworks, to provide provably optimal solutions, and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases. With the accumulation of gene- interaction information and genomic data, we believe that our method can find wide applications in human disease research.
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653 $a False discovery rates
653 $a Mutation
653 $a Driver genes
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710 2 $a State University of New York at Buffalo. $b Computer Science and Engineering. $3 1035503
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793 $a English
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