binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions
Abstract Background In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or hum...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-08-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://link.springer.com/article/10.1186/s12859-020-03718-9 |