Name
Abstract | ||
|---|---|---|
In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13040-018-0168-6 | BioData Mining |
Keywords | Field | DocType |
Epistasis,Feature selection,Non-additive effects,Non-parametric methods,Obesity,Parametric methods | Feature selection,Biology,Epistasis,Parametric statistics,Artificial intelligence,Genetics,Sample size determination,Limiting,Machine learning,Gradient boosting | Journal |
Volume | Issue | ISSN |
11 | 1 | 1756-0381 |
Citations | PageRank | References |
4 | 0.41 | 15 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shefali S. Verma | 1 | 5 | 1.78 |
| Anastasia Lucas | 2 | 5 | 2.11 |
| Xinyuan Zhang | 3 | 4 | 0.41 |
| Yogasudha Veturi | 4 | 4 | 1.42 |
| Scott M. Dudek | 5 | 206 | 26.27 |
| Binglan Li | 6 | 4 | 0.41 |
| Ruowang Li | 7 | 18 | 4.15 |
| Ryan J. Urbanowicz | 8 | 308 | 23.94 |
| Jason H. Moore | 9 | 1223 | 159.43 |
| Dokyoon Kim | 10 | 70 | 6.32 |
| Marylyn D. Ritchie | 11 | 692 | 86.79 |