چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| سپیده زنونی واحد | اول |
| محمد رضا اردلان | دوم |
| سید مهدی حسینی یان خطیبی | سوم |
| سعید پیرمرادی | چهارم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Introduction: Wilms tumor (WT) and rhabdoid tumor of the kidney (RT) are respectively the most and less common types of pediatric kidney tumors. Due to the presence of overlapping histologic patterns and similar cell types across these tumors, their differential diagnosis solely based on histologic study can be challenging. To this end, this study aimed to apply machine learning and deep learning algorithms to identify the most important mRNAs and microRNAs panels that can be involved in the pathogenesis of the WT and RT. Methods: The RNA transcripts including 1881 microRNA (miRNAs) and 60,482 mRNAs obtained from 126 and 199 patients, respectively, were downloaded from The Cancer Genome Atlas (TCGA) dataset. To identify candidate features (mRNAs and miRNAs), graph and filter algorithms were used in feature selection. Then, a deep model was used to classify the tumors. Finally, an association rule mining algorithm was used for detecting the most significant mRNAs/ miRNAs involved in the pathogenesis of the WT and RT. Results: In the classification step, candidate miRNAs could classify the WT and RT classes in train/test data with high accuracy (97% / 93%). Candidate mRNAs could also classify the WT and RT classes in train/ test data with high accuracy (94% / 97%) and AUC ($0.95). The Association Rule Mining analysis could identify the Chromosome 19 open reading frame 24 (C19orf24) and let-7a-2 as well as the RP1-3E10.2 and miR-199b as first top transcripts in the WT and RT, respectively. Conclusions: The employed framework can offer further insight into the pathogenesis, diagnosis, prognosis, and therapeutic targets in pediatric kidney tumors. |
| کلمات کلیدی | MRNA, MICRORNA, WILMS TUMOR, RHABDOID TUMOR, KIDNEY, RULE MINING |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Wilms Abstract KIR.pdf | 1400/12/23 | 99123 | دانلود |
| Wilms WCN certification.pdf | 1400/12/23 | 498203 | دانلود |
