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Pathway-Based Multi-Omics Data Integ...

Huang, Sijia.

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Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis./
作者:	Huang, Sijia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	191 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10805657
ISBN:	9780355825497

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.
Huang, Sijia.

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 191 p.

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--University of Hawai'i at Manoa, 2017.

With the increasing awareness of heterogeneity in breast cancers, better predictions of breast cancer diagnosis and prognosis are important components of precision medicine. High-throughput profiles have been explored extensively in the last decades for diagnostic and prognostic biomarkers in breast cancer. However, different omics-based studies show little overlap results. With the abundance of multi-omics measurements for cancer patients, there is pressing need for integrative methods that can take advantage of biological information at different biological layers and extract the concerted mechanism in breast cancer.

ISBN: 9780355825497Subjects--Topical Terms:

553671
Bioinformatics.

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
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520 $a With the increasing awareness of heterogeneity in breast cancers, better predictions of breast cancer diagnosis and prognosis are important components of precision medicine. High-throughput profiles have been explored extensively in the last decades for diagnostic and prognostic biomarkers in breast cancer. However, different omics-based studies show little overlap results. With the abundance of multi-omics measurements for cancer patients, there is pressing need for integrative methods that can take advantage of biological information at different biological layers and extract the concerted mechanism in breast cancer.
520 $a Towards this goal we propose a new class of pathway-based diagnosis and prognosis prediction models, which emphasize individualized pathway-based risk measurement using the pathway dysregulation scores. We hypothesize that higher-level pathway-based models will consistently perform better than gene- or metabolites- based models. Towards this we have obtained some promising preliminary results, using pathway-based features from transcriptomics data to predict breast cancer prognosis, as well as from metabolomics data to predict breast cancer diagnosis. Next we applied this methodology together with deep learning approach to integrate multi-omics data (gene expression, methylation and copy number variation) for breast cancer patients from public resources such as TCGA and METABRIC, for the purposes of identifying breast cancer subpopulations with prognosis differences. Our results showed that not only our pathway-based prediction consistently performs better than raw data based prediction, but also our deep-learning based integration method gives a better characterization of different cancer subgroups compared to current state-of-art method.
520 $a In this thesis the significance of pathway-based biomarkers in breast cancer was characterized, from genomics, metabolomics to multi-omics level. In chapter 1, I further explain the breast cancer diagnosis and prognosis background relevant to the projects contained in this dissertation. Chapter 2 is a research paper published in Genome Medicine, using pathway-based approach on metabolomics data to discover biomarkers for breast cancer diagnosis. In Chapter 3, we applied our pathway-based pipeline on transcriptomics data, to predict for breast cancer prognosis; this work is published in PLOS Computation Biology. Chapter 4 is a trial of integrating clinical traits with biomarkers to evaluate the risk of bladder cancer diagnosis, published in Cancer Epidemiology, Biomarkers and Prevention. This work brings the promising value of integrating more than one levels of information to predict the cancer outcome. Chapter 5 is a review paper published in Frontiers in Genetics, focusing on the current work of multi-omics data integration, summarizing the diverse computational tools developed over the years, their advantages and limitations. In Chapter 6, I extend the pathway-based pipeline to multi-omics data based a deep-learning model, in order to predict patient survival, and to elucidate the biological pathways relevant to each patient. Finally, in Chapter 7, I discuss what these research projects have accomplished in the grand scheme of the breast cancer research field, and explain what further work needs to be accomplished to follow up. In the future, we plan to validate the significant pathway biomarkers and discover the relationship of medicines with pathways to predict for better and personalized therapeutics treatment in breast cancer.
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