Name
Title | ||
|---|---|---|
Transition-transversion encoding and genetic relationship metric in ReliefF feature selection improves pathway enrichment in GWAS. |
Abstract | ||
|---|---|---|
Our results suggest that using more genetically motivated encodings, such as transition/transversion, and metrics that adjust for allele frequency heterogeneity, such as GRM, lead to ReliefF attribute scores with improved pathway enrichment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13040-018-0186-4 | BioData Mining |
Keywords | Field | DocType |
Feature selection,Genetic relationship matrix (GRM),Genome-wide association study (GWAS),Machine learning,Transition and transversion | Data mining,Transversion,Allele,Feature selection,Epistasis,Computer science,Allele frequency,Lasso (statistics),Genome-wide association study,Computational biology,Random forest | Journal |
Volume | Issue | ISSN |
11 | 1 | 1756-0381 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Arabnejad | 1 | 0 | 0.34 |
| B. A. Dawkins | 2 | 0 | 0.34 |
| William S. Bush | 3 | 161 | 18.45 |
| Bill C. White | 4 | 20 | 3.09 |
| A. R. Harkness | 5 | 0 | 0.34 |
| Brett A. McKinney | 6 | 74 | 7.36 |