دورية أكاديمية
A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy
| العنوان: | A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy |
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| المؤلفون: | Qi-en He MS, Yi-fan Tong MS, Zhou Ye MD, Li-xia Gao MD, Yi-zhi Zhang MD, Ling Wang MD, Kai Song PhD |
| المصدر: | Technology in Cancer Research & Treatment, Vol 19 (2020) |
| بيانات النشر: | SAGE Publishing, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: | Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: | Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1533-0338 15330338 |
| Relation: | https://doaj.org/toc/1533-0338 |
| DOI: | 10.1177/1533033820909112 |
| URL الوصول: | https://doaj.org/article/b6aca3db0dce4d06aee5f8b09861f608 |
| رقم الأكسشن: | edsdoj.b6aca3db0dce4d06aee5f8b09861f608 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 15330338 |
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| DOI: | 10.1177/1533033820909112 |