چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| فرزانه حمیدی | اول |
| ندا گیلانی | دوم |
| رضا عربی بلاغی | سوم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | LASSO, Elastic Net, Feature Selection, Ovarian Cancer, Biomarker, Machine Learning, Expression profiling |
| خلاصه مقاله | MicroRNAs (miRNAs) are a type of non-coding RNAs with 18–25 nucleotides in length and reported to play serious roles in human cancers. Ovarian cancer is a major clinical challenge in gynecologic oncology. The discovery of new early biomarkers for OC may improve the patients' response to the treatment. By using LASSO and Elastic Net, the present study identified 10 miRNAs were up-regulated in ovarian serum cancer samples compared to non-cancer samples, including has-miR-5100, has-miR-6800-5p, has-miR-1233-5p, has-miR-4532, has-miR-4783-3p, has-miR-4787-3p, has-miR-1228-5p, has-miR-1290, has-miR-3184-5p and has-miR-320b. Therefore, the diagnostic capacity of miRNAs among the 10 candidates was evaluated for two data sets GSE106817 and GSE113486 by ROC analysis. We used five of the stat of the art of machine learning algorithms to predict Ovarian Cancer and 4 models yielded an AUC of 100%. Our findings provide robust evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| ICAHT conference.pdf | 1401/02/01 | 1008779 | دانلود |
| gahavahi.pdf | 1401/02/28 | 1603809 | دانلود |
| Exploration of MicroRNAs as Novel Prognostic Biomarkers in Ovarian Cancer Using Artificial intelligence.pdf | 1401/02/28 | 1155675 | دانلود |
