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Model for predicting/screening small molecules with ability to achieve specific gene expression profile

(2020.04 - 2020.05)
 Performance:

1. model selection, and biological meaning interperation;

(2020.01.19 - 2020.02)
 ref: https://github.com/google-research/disentanglement_lib

1. models: β-VAE, FactorVAE, β-TCVAE, DIP-VAE
2. metrics: BetaVAE score, FactorVAE score, Mutual Information Gap(MIG), DCI disentanglement
3. dataset: L1000 gene expression subset.

(2020.02 - 2020.03)
 model conducted: beta-VAE, AnnealedVAE, beta-TCVAE, FactorVAE, DIP-VAE;
 metrics used:

1. Independence between the latent variables - total correlation/correlation between variables;
2. capacity of latent variable - KL(q(z_i|x)||p(z_i));
3. Mutual information between the latent variables and the data variable;

Done by Pengcheng.

Dataset: to check performance on test set 1) from the same distribution with training set; 2) novel cell type/small molecule; loss: E(logp(yi,xi,zi)|zx,zd) = E(logp(yi,xi,zi|yi^,xi^,zi^)) ~ E(MSE). concat or not before MSE? => equivalent
Evaluation: metrics design
maybe worth trying: Does disentanglement performance correlated with our prediction result?