Research map / Probabilistic and scientific modeling / Molecules and proteins
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use c-GPLVM instead of LVR | Latent-variable recovery · Synthetic gene expression, linear (ADD) | correlation with true z: 0.99 -> 0.99 | No clear effect |
| Use c-GPLVM instead of LVR | Latent-variable recovery · Synthetic gene expression, linear (INT) | correlation with true z: 0.97 -> 0.97 | No clear effect |
| Use c-GPLVM instead of LVR | Latent-variable recovery · Synthetic gene expression, monotone | correlation with true z: 0.91 -> 0.96 | Helped |
| Use c-GPLVM instead of GPLVM | Latent-variable recovery · Synthetic gene expression, monotone+transient | correlation with true z: 0.91 -> 0.97 | Helped |
| Use c-GPLVM instead of Weibull regression | Survival prediction · TCGA breast cancer, artificial censoring | MSE: 17.1 -> 9.2 | Helped |
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