Name
Abstract | ||
|---|---|---|
Cancer is one of the major diseases that threaten human life. The advancement of high-throughput sequencing technology provides a way to accurately diagnose cancer and reveal the pathogenesis of cancer at the molecular level. In this study, we integrated the differentially expressed genes, and differential DNA methylation patterns, and applied multiple machine learning methods to conduct cancer diagnosis. The experimental results show that the performance of cancer diagnosis can be significantly improved with the integrated multi-scale gene features of RNA and epigenetic level. The AUC of classifier can be increased by 7.4% with multi-scale gene features compared to only differentially expressed genes, which verifies the effectiveness of the integration of multi-scale gene features for cancer diagnosis. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-97909-0_67 | BIOMETRIC RECOGNITION, CCBR 2018 |
Keywords | Field | DocType |
Cancer diagnosis,Machine learning,Gene expression,DNA methylation,High-Throughput sequencing technology | RNA,Gene,Biology,Gene expression,Pathogenesis,DNA methylation,Computational biology,Cancer,Epigenetics | Conference |
Volume | ISSN | Citations |
10996 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Hang | 1 | 0 | 0.34 |
| Mengjun Shi | 2 | 0 | 0.34 |
| Quan Long | 3 | 0 | 0.34 |
| Hui Li | 4 | 202 | 34.25 |
| Haifeng Zhao | 5 | 1 | 4.10 |
| Meng Ma | 6 | 82 | 12.29 |