Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning
Abstract Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-88548-2 |