Gene selection algorithm by combining reliefF and mRMR
2008
Gene Selection Algorithm Combining ReliefF and mRMR
Sample size: 248
publication
Evidence: high
Author Information
Author(s): Zhang Yi, Ding Chris, Li Tao
Primary Institution: Florida International University
Hypothesis
Can combining ReliefF and mRMR improve gene selection for biological samples?
Conclusion
The mRMR-ReliefF gene selection algorithm is very effective.
Supporting Evidence
- The mRMR-ReliefF selection algorithm leads to significantly improved class predictions.
- Gene selection improves class prediction.
- The mRMR-ReliefF algorithm achieves better performance compared to other gene selection algorithms.
Takeaway
This study created a new way to pick important genes from a large group, making it easier to understand different types of cells.
Methodology
A two-stage selection algorithm combining ReliefF and mRMR was used to select genes from various datasets.
Limitations
The mRMR method is computationally expensive and may not work well with large datasets.
Digital Object Identifier (DOI)
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