A novel approach for identifying relevant genes for breast cancer survivability on specific therapies
Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this work, we use supervised...
Đã lưu trong:
| Những tác giả chính: | , , , |
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| Định dạng: | Journal article |
| Ngôn ngữ: | English |
| Được phát hành: |
SAGE Publications
2023
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2709 |
| Các nhãn: |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Tóm tắt: | Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body
response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this
work, we use supervised and nonsupervised machine learning methods to deal with a multiclass classification problem in which we label the
samples based on the combination of the 5-year survivability and treatment; we focus on hormone therapy, radiotherapy, and surgery. The
proposed nonsupervised hierarchical models are created to find the highest separability between combinations of the classes. The supervised
model consists of a combination of feature selection techniques and efficient classifiers used to find a potential set of biomarker genes
specific to response to therapy. The results show that different models achieve different performance scores with accuracies ranging from
80.9% to 100%. We have investigated the roles of many biomarkers through the literature and found that some of the discriminative genes in
the computational model such as ZC3H11A, VAX2, MAF1, and ZFP91 are related to breast cancer and other types of cancer |
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