Graph designing RNAs: a junction tree decomposed variational auto-encoding approach

This is a repository dedicated to the task of RNA inverse design, following a tree decomposed graph variational auto-encoding approach.

More information will be added as this project gradually moves towards its completion.

Training mandates teacher forcing, whereas an autoregressive decoding scheme is recommended at the inference stage.

Main reference:

Name		Name	Last commit message	Last commit date
Latest commit History 72 Commits
baseline_models		baseline_models
cnf_models		cnf_models
emb_models		emb_models
jtvae_models		jtvae_models
lib		lib
model/__pycache__		model/__pycache__
pics		pics
supervised_encoder_models		supervised_encoder_models
README.md		README.md
create_htr_selex_dataset.py		create_htr_selex_dataset.py
create_rnacompete_s_dataset.py		create_rnacompete_s_dataset.py
jtvae_property_optimization.py		jtvae_property_optimization.py
kmer_content.py		kmer_content.py
preprocess.py		preprocess.py
rigourous_posterior_prior.py		rigourous_posterior_prior.py
train_graph_baseline.py		train_graph_baseline.py
train_htr_selex_fully_supervised_regressor.py		train_htr_selex_fully_supervised_regressor.py
train_jtvae.py		train_jtvae.py
train_lstm_baseline_flow_prior.py		train_lstm_baseline_flow_prior.py
train_rnacompete_S_emb_regressor.py		train_rnacompete_S_emb_regressor.py
train_rnacompete_S_fully_supervised_regressor.py		train_rnacompete_S_fully_supervised_regressor.py
train_rnacompete_emb_regressor.py		train_rnacompete_emb_regressor.py
train_rnacompete_fully_supervised_regressor.py		train_rnacompete_fully_supervised_regressor.py
train_supervised_jtvae_on_rnacompete.py		train_supervised_jtvae_on_rnacompete.py
train_supervised_jtvae_on_rnacompete_S.py		train_supervised_jtvae_on_rnacompete_S.py

HarveyYan/GraphDesigningRNAs