ingest cdrApp 2017-07-06T11:49:50.882Z f47fee2b-b335-4530-8fc6-0075e2c9b39d modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T11:58:16.152Z Setting exclusive relation modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T11:58:24.440Z Setting exclusive relation addDatastream MD_TECHNICAL fedoraAdmin 2017-07-06T11:58:32.675Z Adding technical metadata derived by FITS modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T11:58:48.643Z Setting exclusive relation addDatastream MD_FULL_TEXT fedoraAdmin 2017-07-06T11:58:57.289Z Adding full text metadata extracted by Apache Tika modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-07-06T11:58:58.329Z Setting exclusive relation modifyDatastreamByValue RELS-EXT cdrApp 2017-07-06T12:26:39.728Z Setting exclusive relation modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-04T16:36:06.187Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-25T11:06:30.747Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-27T11:13:14.548Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-03-14T08:05:11.278Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-05-17T19:36:21.399Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-11T06:38:05.926Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-18T02:49:17.063Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-16T16:00:41.848Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-27T02:28:36.408Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-10-12T03:00:18.348Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2019-03-20T21:28:52.518Z Kyla Collins Author Curriculum in Bioinformatics and Computational Biology School of Medicine DATA-DRIVEN COMPUTATIONAL MODELING OF THE STATE AND ARCHITECTURE OF THE BREAST CANCER KINOME The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. Spring 2017 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text Kyla Collins Author Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. Spring 2017 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. Spring 2017 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. Spring 2017 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017-05 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 University of North Carolina at Chapel Hill Degree granting institution Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 University of North Carolina at Chapel Hill Degree granting institution Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine Data-Driven Computational Modeling of the State and Architecture of the Breast Cancer Kinome The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Bioinformatics and Computational Biology Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 Kyla Collins Creator Curriculum in Bioinformatics and Computational Biology School of Medicine The complex signaling in the kinome provides a unique insight into breast cancer, which is heterogeneous with many disease states or subtypes. The kinome has been implicated in many cancers and is highly targeted by inhibitor therapies because of its importance in cell proliferation and differentiation. High-throughput data sets using proteomics help characterize the kinome and allow quantification of the baseline and perturbed states of the kinome. These high-throughput experimental methods allow for quantification of kinases that are not well-studied, or are understudied. In this thesis, I employ machine-learning techniques to distinguish between breast cancer subtypes using a functional proteomics data set and to demonstrate that the state of the kinome looks different in proteomic and sequencing data sets. Characterized, as well as understudied, kinases are identified as important features in stratifying unperturbed breast cancer subtypes. In addition, both understudied and characterized kinases respond dynamically across breast cancer subtypes in response to kinase inhibitor therapy treatment. Further, I developed computational methodologies to characterize the architecture of the kinome network and an optimization method for choosing effective combination therapies for cancer treatment. Public protein-protein interaction databases are compiled to create the comprehensive kinome network, consisting of only kinase to kinase interactions. The comprehensive kinome network is clustered to identify functional modules, or subnetworks, and some of these subnetworks are significantly enrichment for understudied and targeted kinases. In addition, the optimization proposed here provides a computational framework for choosing effective sets of inhibitors to use concurrently, i.e. combination therapies. 2017 Bioinformatics Biology eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Shawn Gomez Thesis advisor Gary Johnson Thesis advisor Timothy Elston Thesis advisor Lee Graves Thesis advisor Zefeng Wang Thesis advisor text 2017-05 Collins_unc_0153D_16900.pdf uuid:89eae86b-df21-4425-8135-6fbf1cac8ccf 2019-07-06T00:00:00 2017-04-24T17:45:04Z proquest application/pdf 11214438 yes