Timeline for Feature selection by lasso and cross validation of model with low sample number
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 3, 2019 at 14:20 | vote | accept | Kent | ||
| Nov 29, 2019 at 20:53 | answer | added | EdM | timeline score: 1 | |
| Nov 28, 2019 at 16:00 | comment | added | Kent | @EdM My goal is to see if within these group they have features that are similar enough I could actually call them groups. Because the label now is based on their cytogenetics abnormality. It might be relevant to their gene expression pattern, but it might not as well. I did a clustering with top 5% genes with highest MAD, but with the small number of samples I got advice that a supervised feature selection approach may be better, so I want to see if lasso is also useful. | |
| Nov 28, 2019 at 15:48 | comment | added | Kent | @DemetriPananos Thanks for the reply! I would love to but they are rare subtype of a disease. My personal thought is that the medical science community will need more data scientists than ever to help developing methods with this p>>n data structure. I am not sure if it is possible. | |
| Nov 27, 2019 at 15:00 | comment | added | EdM | Please say more about the scientific goal of your study. It's possible that some approach based on representation of sets of genes related to specific biological functions or biochemical pathways might be more useful than trying to pull out a small number of individual genes. But that would depend on what you are trying to accomplish. | |
| Nov 27, 2019 at 14:53 | comment | added | Demetri Pananos | Usually Lasso is a good first step, but with three orders of magnitude more features than observations, I think you should instead collect more data. | |
| Nov 27, 2019 at 14:51 | history | asked | Kent | CC BY-SA 4.0 |