Combined performance of screening and variable selection methods in ultra-high dimensional data in predicting time-to-event outcomes
Abstract Background Building prognostic models of clinical outcomes is an increasingly important research task and will remain a vital area in genomic medicine. Prognostic models of clinical outcomes are usually built and validated utilizing variable selection methods and machine learning tools. The...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-09-01
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| Series: | Diagnostic and Prognostic Research |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s41512-018-0043-4 |