Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data
Background: Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions – approaches which are susceptible to misrepresentation and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-11-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/11769351211056298 |