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 an...
Đã lưu trong:
| Những tác giả chính: | , , , , , |
|---|---|
| Định dạng: | Journal article |
| Ngôn ngữ: | English |
| Được phát hành: |
2023
|
| Những chủ đề: | |
| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2633 |
| Các nhãn: |
Thêm thẻ
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
|---|
| id |
oai:scholar.dlu.edu.vn:123456789-2633 |
|---|---|
| record_format |
dspace |
| spelling |
oai:scholar.dlu.edu.vn:123456789-26332023-12-13T03:36:31Z A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies Tabl, Ashraf Abou Alkhateeb, Abedalrhman Phạm, Quang Huy Rueda, Luis ElMaraghy, Waguih Ngom, Alioune breast cancer; classification; feature selection; gene biomarkers; machine learning; survival; treatment therapy 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. 2023-06-14T07:18:32Z 2023-06-14T07:18:32Z 2018 Journal article Bài báo đăng trên tạp chí quốc tế (có ISSN), bao gồm book chapter 1176-9343 https://scholar.dlu.edu.vn/handle/123456789/2633 10.1177/1176934318790266 30116102 en Evolutionary bioinformatics online |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
breast cancer; classification; feature selection; gene biomarkers; machine learning; survival; treatment therapy |
| spellingShingle |
breast cancer; classification; feature selection; gene biomarkers; machine learning; survival; treatment therapy Tabl, Ashraf Abou Alkhateeb, Abedalrhman Phạm, Quang Huy Rueda, Luis ElMaraghy, Waguih Ngom, Alioune A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies |
| description |
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. |
| format |
Journal article |
| author |
Tabl, Ashraf Abou Alkhateeb, Abedalrhman Phạm, Quang Huy Rueda, Luis ElMaraghy, Waguih Ngom, Alioune |
| author_facet |
Tabl, Ashraf Abou Alkhateeb, Abedalrhman Phạm, Quang Huy Rueda, Luis ElMaraghy, Waguih Ngom, Alioune |
| author_sort |
Tabl, Ashraf Abou |
| title |
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies |
| title_short |
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies |
| title_full |
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies |
| title_fullStr |
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies |
| title_full_unstemmed |
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies |
| title_sort |
novel approach for identifying relevant genes for breast cancer survivability on specific therapies |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2633 |
| _version_ |
1785973189591957504 |