Variable selection for disease progression models: methods for oncogenetic trees and application to cancer and HIV
Abstract Background Disease progression models are important for understanding the critical steps during the development of diseases. The models are imbedded in a statistical framework to deal with random variations due to biology and the sampling process when observing only a finite population. Con...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2017-08-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1762-1 |