| Accession |
PRJCA002437 |
| Title |
Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma |
| Relevance |
Medical |
| Data types |
Whole genome sequencing
|
| Organisms |
Homo sapiens
|
| Description |
In this study, we conducted whole genome sequencing (WGS) using 384 plasma samples and developed a somatic copy number aberration (SCNA)-based, machine learning-driven statistical model for the non-invasive detection of early-stage HCC. We demonstrated the robust high performance of the model through strict independent validations. |
| Sample scope |
Multiisolate |
| Release date |
2020-03-24 |
| Grants |
| Agency |
program |
Grant ID |
Grant title |
| National Natural Science Foundation of China
|
|
81320108021
|
|
|
|
| Submitter |
Jinliang
Xing (xingjinliang@163.com)
|
| Organization |
State Key Laboratory of Cancer Biology |
| Submission date |
2020-03-24 |
