Đang chuẩn bị liên kết để tải về tài liệu:
Predicting the recurrence of noncoding regulatory mutations in cancer

Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ

One of the greatest challenges in cancer genomics is to distinguish driver mutations from passenger mutations. Whereas recurrence is a hallmark of driver mutations, it is difficult to observe recurring noncoding mutations owing to a limited amount of whole-genome sequenced samples. |

TÀI LIỆU LIÊN QUAN

KIBRA; a novel biomarker predicting recurrence free survival of breast cancer patients receiving adjuvant therapy

Nomograms for predicting risk of locoregional recurrence and distant metastases for esophageal cancer patients after radical esophagectomy

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

Establishment and validation of a nomogram with intratumoral heterogeneity derived from 18F-FDG PET/CT for predicting individual conditional risk of 5-year recurrence before initial treatment of nasopharyngeal carcinoma

PD-L1 tumour expression is predictive of pazopanib response in soft tissue sarcoma

Bipolar energy for transurethral resection of non muscle invasive bladder cancer - histopathological and predicting disease recurrence

Tumor and peritumor radiomics analysis based on contrast-enhanced CT for predicting early and late recurrence of hepatocellular carcinoma after liver resection

A nomogram combining clinical factors and biomarkers for predicting the recurrence of high-risk cutaneous squamous cell carcinoma

The usefulness of the total metabolic tumor volume for predicting the postoperative recurrence of thoracic esophageal squamous cell carcinoma