XieResearchGroup / CLEIT Public

Cross-LEvel Information Transmission network (CLEIT) aims to represent the asymmetrical multi-level organization of the biological system by integrating multiple incoherent omics data. It first learns the latent representation of the high-level domain then uses it as ground-truth embedding to improve the representation learning of the low-level …

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Name		Name	Last commit message	Last commit date
Latest commit History 273 Commits
figs		figs
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
ae.py		ae.py
ae_main.py		ae_main.py
base_ae.py		base_ae.py
build_reference_encoder.py		build_reference_encoder.py
da_main.py		da_main.py
data.py		data.py
data_config.py		data_config.py
dsn_ae.py		dsn_ae.py
encoder_decoder.py		encoder_decoder.py
evaluation_utils.py		evaluation_utils.py
fine_tuning.py		fine_tuning.py
gradient_reversal.py		gradient_reversal.py
hyper_main.py		hyper_main.py
loss_and_metrics.py		loss_and_metrics.py
mask_mlp.py		mask_mlp.py
masked_linear.py		masked_linear.py
ml_baseline.py		ml_baseline.py
mlp.py		mlp.py
mlp_main.py		mlp_main.py
multi_out_mlp.py		multi_out_mlp.py
parsing_utils.py		parsing_utils.py
preprocess_ccle_gdsc_utils.py		preprocess_ccle_gdsc_utils.py
preprocess_xena_utils.py		preprocess_xena_utils.py
preprocessing.py		preprocessing.py
propagation_utils.py		propagation_utils.py
train_adda.py		train_adda.py
train_ae.py		train_ae.py
train_cleit.py		train_cleit.py
train_cleita.py		train_cleita.py
train_cleitc.py		train_cleitc.py
train_cleitm.py		train_cleitm.py
train_coral.py		train_coral.py
train_dann.py		train_dann.py
train_dcc.py		train_dcc.py
train_dsn.py		train_dsn.py
train_params.json		train_params.json
train_vae.py		train_vae.py
types_.py		types_.py
utils.py		utils.py
vae.py		vae.py
vae_main.py		vae_main.py

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CLEIT

Data Source

depmap: https://depmap.org/portal/download/
xena: http://xena.ucsc.edu/public/
gdsc: https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html

CLEIT framework

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Cross-LEvel Information Transmission network (CLEIT) aims to represent the asymmetrical multi-level organization of the biological system by integrating multiple incoherent omics data. It first learns the latent representation of the high-level domain then uses it as ground-truth embedding to improve the representation learning of the low-level …

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