A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability
Identifying biomarkers for better diagnosis or prognosis of breast cancer is in demand but presents many challenges. In this study, we introduced a data-integration approach to identify sub-network biomarkers capable of predicting breast cancer treatment outcomes including disease-free survival, and...
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| Định dạng: | Conference paper |
| Ngôn ngữ: | English |
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ACM
2023
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2705 |
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oai:scholar.dlu.edu.vn:123456789-27052023-12-13T03:25:24Z A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability Phạm, Quang Huy Jurko, Guba Mousa, Gawanmeh Alioune, Ngom Lisa, A. Porter Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification Breast cancer survivability prediction Identifying biomarkers for better diagnosis or prognosis of breast cancer is in demand but presents many challenges. In this study, we introduced a data-integration approach to identify sub-network biomarkers capable of predicting breast cancer treatment outcomes including disease-free survival, and overall survival at five years and long-term. A gene expression data is used for evaluating the predictive power of sub-networks of genes, while the protein-protein interaction network is to guide the search for the candidate sub-networks. To reduce the search space, we proposed a score to estimate the predictive ability of a set of genes, thus, only the candidates with the high score are evaluated by Support Vector Machine classifier during the search. After the sub-networks with the highest classification performance were selected for all seed genes, they were further analyzed with pathway data and cancer-related genes from literature for their biological meaning. The selected sub-networks yielded highly accurate and contained genes associated with many cancer pathways, including breast cancer. 639–644 2023-06-14T16:11:27Z 2023-06-14T16:11:27Z 2019 Conference paper Bài báo đăng trên KYHT quốc tế (có ISBN) https://scholar.dlu.edu.vn/handle/123456789/2705 10.1145/3307339.3343480 en The 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics ACM ACM Digital Library |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification Breast cancer survivability prediction |
| spellingShingle |
Data integration Sub-network selection Sub-network biomarkers Breast cancer biomarkers Survivability prediction Network-based classification Breast cancer survivability prediction Phạm, Quang Huy Jurko, Guba Mousa, Gawanmeh Alioune, Ngom Lisa, A. Porter A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability |
| description |
Identifying biomarkers for better diagnosis or prognosis of breast cancer is in demand but presents many challenges. In this study, we introduced a data-integration approach to identify sub-network biomarkers capable of predicting breast cancer treatment outcomes including disease-free survival, and overall survival at five years and long-term. A gene expression data is used for evaluating the predictive power of sub-networks of genes, while the protein-protein interaction network is to guide the search for the candidate sub-networks. To reduce the search space, we proposed a score to estimate the predictive ability of a set of genes, thus, only the candidates with the high score are evaluated by Support Vector Machine classifier during the search. After the sub-networks with the highest classification performance were selected for all seed genes, they were further analyzed with pathway data and cancer-related genes from literature for their biological meaning. The selected sub-networks yielded highly accurate and contained genes associated with many cancer pathways, including breast cancer. |
| format |
Conference paper |
| author |
Phạm, Quang Huy Jurko, Guba Mousa, Gawanmeh Alioune, Ngom Lisa, A. Porter |
| author_facet |
Phạm, Quang Huy Jurko, Guba Mousa, Gawanmeh Alioune, Ngom Lisa, A. Porter |
| author_sort |
Phạm, Quang Huy |
| title |
A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability |
| title_short |
A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability |
| title_full |
A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability |
| title_fullStr |
A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability |
| title_full_unstemmed |
A Network-based Machine Learning Approach for Identifying Biomarkers of Breast Cancer Survivability |
| title_sort |
network-based machine learning approach for identifying biomarkers of breast cancer survivability |
| publisher |
ACM |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2705 |
| _version_ |
1785973243767685120 |