A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability

We introduce a network-based approach to identify subnets of functionally-related genes for predicting 5-year survivability of breast cancer patients treated with chemotherapy, hormone therapy, and a combination of these. A gene expression dataset and a protein-protein interaction network are integr...

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Những tác giả chính:	Phạm, Quang Huy, Luis Rueda, Alioune, Ngom
Định dạng:	Conference paper
Ngôn ngữ:	Vietnamese
Được phát hành:	Springer International Publishing 2023
Những chủ đề:	Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification
Truy cập trực tuyến:	https://scholar.dlu.edu.vn/handle/123456789/2707
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spelling	oai:scholar.dlu.edu.vn:123456789-27072023-06-14T16:36:40Z A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability Phạm, Quang Huy Luis Rueda Alioune, Ngom Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification We introduce a network-based approach to identify subnets of functionally-related genes for predicting 5-year survivability of breast cancer patients treated with chemotherapy, hormone therapy, and a combination of these. A gene expression dataset and a protein-protein interaction network are integrated to construct a weighted graph, where edge weight expresses the predictability of the two corresponding genes in predicting the class. We propose a scoring criterion to measure the density of a weighted sub-graph, which is also an estimation of its predictive power. Thus, we can identify an optimally-dense sub-network for each seed gene, and then evaluate that sub-network by classification method. Finally, among the sub-networks whose classification performance greater than a given threshold, we search for an optimal set of sub-networks that can further improve classification performance via a voting scheme. We significantly improved the results of existing approaches. For each type of treatment, our best prediction model can reach 85% accuracy or more. Many selected sub-networks used to construct the voting models contain breast/other cancer-related genes including SP1, TP53, MYC, NOG, and many more, providing pieces of evidence for down-stream analysis. 49-60 2023-06-14T16:36:33Z 2023-06-14T16:36:33Z 2020 Conference paper Bài báo đăng trên KYHT quốc tế (có ISBN) https://scholar.dlu.edu.vn/handle/123456789/2707 10.1007/978-3-030-45385-5_5 vi International Work-Conference on Bioinformatics and Biomedical Engineering, 2020 Springer International Publishing
institution	Thư viện Trường Đại học Đà Lạt
collection	Thư viện số
language	Vietnamese
topic	Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification
spellingShingle	Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification Phạm, Quang Huy Luis Rueda Alioune, Ngom A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability
description	We introduce a network-based approach to identify subnets of functionally-related genes for predicting 5-year survivability of breast cancer patients treated with chemotherapy, hormone therapy, and a combination of these. A gene expression dataset and a protein-protein interaction network are integrated to construct a weighted graph, where edge weight expresses the predictability of the two corresponding genes in predicting the class. We propose a scoring criterion to measure the density of a weighted sub-graph, which is also an estimation of its predictive power. Thus, we can identify an optimally-dense sub-network for each seed gene, and then evaluate that sub-network by classification method. Finally, among the sub-networks whose classification performance greater than a given threshold, we search for an optimal set of sub-networks that can further improve classification performance via a voting scheme. We significantly improved the results of existing approaches. For each type of treatment, our best prediction model can reach 85% accuracy or more. Many selected sub-networks used to construct the voting models contain breast/other cancer-related genes including SP1, TP53, MYC, NOG, and many more, providing pieces of evidence for down-stream analysis.
format	Conference paper
author	Phạm, Quang Huy Luis Rueda Alioune, Ngom
author_facet	Phạm, Quang Huy Luis Rueda Alioune, Ngom
author_sort	Phạm, Quang Huy
title	A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability
title_short	A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability
title_full	A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability
title_fullStr	A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability
title_full_unstemmed	A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability
title_sort	data integration approach for detecting biomarkers of breast cancer survivability
publisher	Springer International Publishing
publishDate	2023
url	https://scholar.dlu.edu.vn/handle/123456789/2707
_version_	1778233929604530176

A Data Integration Approach for Detecting Biomarkers of Breast Cancer Survivability

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