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README.md

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-# Pretrained Models
-
-This directory contains pretrained VAE and reconstruction network models obtained during the WP3 of the EVENFLOW EU project.
-
-These models have been trained on a pre-processed version of the bulk RNA-Seq TCGA datasets of either KIRC or BRCA, independently (see data availability in the respective section).
-
-## Available Models
-
-### KIRC (Kidney Renal Clear Cell Carcinoma)
-
-**Location**: `KIRC/`
-
-*Data availability:* [Zenodo](https://doi.org/10.5281/zenodo.17987300)
-
-**Model Files**:
-- `20250321_VAE_idim8516_md512_feat256mse_relu.pth` - VAE weights
-- `network_reconstruction.pth` - Reconstruction network weights
-- `network_dims.csv` - Network architecture specifications
-
-**Model Specifications**:
-- Input dimension: 8,516 genes
-- VAE architecture:
-  - Middle dimension: 512
-  - Latent dimension: 256
-  - Loss function: MSE
-  - Activation: ReLU
-- Reconstruction network: [8954, 3512, 824, 3731, 8954]
-- Training: Beta-VAE with 3 cycles, 600 epochs total
-
-### BRCA (Breast Invasive Carcinoma)
-
-**Location**: `BRCA/`
-
-*Data availability:* [Zenodo](https://doi.org/10.5281/zenodo.17986123)
-
-**Model Files**:
-- `20251209_VAE_idim8954_md1024_feat512mse_relu.pth` - VAE weights
-- `network_reconstruction.pth` - Reconstruction network weights
-- `network_dims.csv` - Network architecture specifications
-
-**Model Specifications**:
-- Input dimension: 8,954 genes
-- VAE architecture:
-  - Middle dimension: 1,024
-  - Latent dimension: 512
-  - Loss function: MSE
-  - Activation: ReLU
-- Reconstruction network: [8954, 3104, 790, 4027, 8954]
-- Training: Beta-VAE with 3 cycles, 600 epochs total
-
-## Usage
-
-### Loading Models in Python
- 
-See [renalprog](https://www.github.com/gprolcastelo/renalprog) for the needed VAE and NetworkReconstruction objects.
-
-
-```python
-import torch
-import pandas as pd
-import json
-from pathlib import Path
-import huggingface_hub as hf
-from renalprog.modeling.train import VAE, NetworkReconstruction
-
-# Configuration
-cancer_type = "KIRC"  # or "BRCA"
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# ============================================================================
-# Load VAE Model
-# ============================================================================
-
-# Download VAE config
-vae_config_path = hf.hf_hub_download(
-    repo_id="gprolcastelo/evenflow_models",
-    filename=f"{cancer_type}/config.json"
-)
-
-# Load configuration
-with open(vae_config_path, "r") as f:
-    vae_config = json.load(f)
-
-print(f"VAE Configuration: {vae_config}")
-
-# Download VAE model weights
-if cancer_type == "KIRC":
-    vae_filename = "KIRC/20250321_VAE_idim8516_md512_feat256mse_relu.pth"
-elif cancer_type == "BRCA":
-    vae_filename = "BRCA/20251209_VAE_idim8954_md1024_feat512mse_relu.pth"
-else:
-    raise ValueError(f"Unknown cancer type: {cancer_type}")
-
-vae_model_path = hf.hf_hub_download(
-    repo_id="gprolcastelo/evenflow_models",
-    filename=vae_filename
-)
-
-# Initialize and load VAE
-model_vae = VAE(
-    input_dim=vae_config["INPUT_DIM"],
-    mid_dim=vae_config["MID_DIM"],
-    features=vae_config["LATENT_DIM"]
-).to(device)
-
-checkpoint_vae = torch.load(vae_model_path, map_location=device, weights_only=False)
-model_vae.load_state_dict(checkpoint_vae)
-model_vae.eval()
-
-print(f"VAE model loaded successfully from {cancer_type}")
-
-# ============================================================================
-# Load Reconstruction Network
-# ============================================================================
-
-# Download network dimensions
-network_dims_path = hf.hf_hub_download(
-    repo_id="gprolcastelo/evenflow_models",
-    filename=f"{cancer_type}/network_dims.csv"
-)
-
-# Load network dimensions
-network_dims = pd.read_csv(network_dims_path)
-layer_dims = network_dims.values.tolist()[0]
-
-print(f"Reconstruction Network dimensions: {layer_dims}")
-
-# Download reconstruction network weights
-recnet_model_path = hf.hf_hub_download(
-    repo_id="gprolcastelo/evenflow_models",
-    filename=f"{cancer_type}/network_reconstruction.pth"
-)
-
-# Initialize and load Reconstruction Network
-model_recnet = NetworkReconstruction(layer_dims=layer_dims).to(device)
-checkpoint_recnet = torch.load(recnet_model_path, map_location=device, weights_only=False)
-model_recnet.load_state_dict(checkpoint_recnet)
-model_recnet.eval()
-
-print(f"Reconstruction Network loaded successfully from {cancer_type}")
-
-# ============================================================================
-# Use the models
-# ============================================================================
-
-# Example: Apply VAE to your data
-# your_data = torch.tensor(your_data_array).float().to(device)
-# with torch.no_grad():
-#     vae_output = model_vae(your_data)
-#     recnet_output = model_recnet(vae_output)
-
-```
-
-## Citation
-
-!!! warning "Warning"
-	This citation is temporary. It will be updated when a pre-print is released.
-
-If you use these pretrained models, please cite:
-
-```bibtex
-@software{renalprog2024,
-  title = {RenalProg: A Deep Learning Framework for Kidney Cancer Progression Modeling},
-  author = {[Guillermo Prol-Castelo, Elina Syrri, Nikolaos Manginas, Vasileos Manginas, Nikos Katzouris, Davide Cirillo, George Paliouras, Alfonso Valencia]},
-  year = {2025},
-  url = {https://github.com/gprolcas/renalprog},
-  note = {Preprint in preparation}
-}
-```
-
-## Training Details
-
-These models were trained using:
-- Random seed: 2023
-- Train/test split: 80/20
-- Optimizer: Adam
-- Learning rate: 1e-4
-- Batch size: 8
-- Beta annealing (for VAE): 3 cycles with 0.5 ratio
-
-## Model Performance
-
-**KIRC Model**:
-- Reconstruction loss (test): ~1.1
-
-**BRCA Model**:
-- Reconstruction loss (test): ~0.9
-
-## License
-
-These pretrained models are provided under the same Apache 2.0 license.
-
-## Contact
-
-For questions about the pretrained models, please:
-1. Check the [documentation](https://gprolcastelo.github.io/renalprog/)
-2. Open an issue on [GitHub](https://github.com/gprolcastelo/renalprog/issues)
-3. Contact the authors
-
----
-
-**Last Updated**: December 2025
-**Version**: 1.0.0-alpha
-
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: tabular-regression
+tags:
+- VAE
+- bioinformatics
+- TCGA
+- ccRCC
+- KIRC
+- cancer
+---
+
+
+# Pretrained Models
+
+This directory contains pretrained VAE and reconstruction network models obtained during the WP3 of the EVENFLOW EU project.
+
+These models have been trained on a pre-processed version of the bulk RNA-Seq TCGA datasets of either KIRC or BRCA, independently (see data availability in the respective section).
+
+## Available Models
+
+### KIRC (Kidney Renal Clear Cell Carcinoma)
+
+**Location**: `KIRC/`
+
+*Data availability:* [Zenodo](https://doi.org/10.5281/zenodo.17987300)
+
+**Model Files**:
+- `20250321_VAE_idim8516_md512_feat256mse_relu.pth` - VAE weights
+- `network_reconstruction.pth` - Reconstruction network weights
+- `network_dims.csv` - Network architecture specifications
+
+**Model Specifications**:
+- Input dimension: 8,516 genes
+- VAE architecture:
+  - Middle dimension: 512
+  - Latent dimension: 256
+  - Loss function: MSE
+  - Activation: ReLU
+- Reconstruction network: [8954, 3512, 824, 3731, 8954]
+- Training: Beta-VAE with 3 cycles, 600 epochs total
+
+### BRCA (Breast Invasive Carcinoma)
+
+**Location**: `BRCA/`
+
+*Data availability:* [Zenodo](https://doi.org/10.5281/zenodo.17986123)
+
+**Model Files**:
+- `20251209_VAE_idim8954_md1024_feat512mse_relu.pth` - VAE weights
+- `network_reconstruction.pth` - Reconstruction network weights
+- `network_dims.csv` - Network architecture specifications
+
+**Model Specifications**:
+- Input dimension: 8,954 genes
+- VAE architecture:
+  - Middle dimension: 1,024
+  - Latent dimension: 512
+  - Loss function: MSE
+  - Activation: ReLU
+- Reconstruction network: [8954, 3104, 790, 4027, 8954]
+- Training: Beta-VAE with 3 cycles, 600 epochs total
+
+## Usage
+
+### Loading Models in Python
+ 
+See [renalprog](https://www.github.com/gprolcastelo/renalprog) for the needed VAE and NetworkReconstruction objects.
+
+
+```python
+import torch
+import pandas as pd
+import json
+from pathlib import Path
+import huggingface_hub as hf
+from renalprog.modeling.train import VAE, NetworkReconstruction
+
+# Configuration
+cancer_type = "KIRC"  # or "BRCA"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ============================================================================
+# Load VAE Model
+# ============================================================================
+
+# Download VAE config
+vae_config_path = hf.hf_hub_download(
+    repo_id="gprolcastelo/evenflow_models",
+    filename=f"{cancer_type}/config.json"
+)
+
+# Load configuration
+with open(vae_config_path, "r") as f:
+    vae_config = json.load(f)
+
+print(f"VAE Configuration: {vae_config}")
+
+# Download VAE model weights
+if cancer_type == "KIRC":
+    vae_filename = "KIRC/20250321_VAE_idim8516_md512_feat256mse_relu.pth"
+elif cancer_type == "BRCA":
+    vae_filename = "BRCA/20251209_VAE_idim8954_md1024_feat512mse_relu.pth"
+else:
+    raise ValueError(f"Unknown cancer type: {cancer_type}")
+
+vae_model_path = hf.hf_hub_download(
+    repo_id="gprolcastelo/evenflow_models",
+    filename=vae_filename
+)
+
+# Initialize and load VAE
+model_vae = VAE(
+    input_dim=vae_config["INPUT_DIM"],
+    mid_dim=vae_config["MID_DIM"],
+    features=vae_config["LATENT_DIM"]
+).to(device)
+
+checkpoint_vae = torch.load(vae_model_path, map_location=device, weights_only=False)
+model_vae.load_state_dict(checkpoint_vae)
+model_vae.eval()
+
+print(f"VAE model loaded successfully from {cancer_type}")
+
+# ============================================================================
+# Load Reconstruction Network
+# ============================================================================
+
+# Download network dimensions
+network_dims_path = hf.hf_hub_download(
+    repo_id="gprolcastelo/evenflow_models",
+    filename=f"{cancer_type}/network_dims.csv"
+)
+
+# Load network dimensions
+network_dims = pd.read_csv(network_dims_path)
+layer_dims = network_dims.values.tolist()[0]
+
+print(f"Reconstruction Network dimensions: {layer_dims}")
+
+# Download reconstruction network weights
+recnet_model_path = hf.hf_hub_download(
+    repo_id="gprolcastelo/evenflow_models",
+    filename=f"{cancer_type}/network_reconstruction.pth"
+)
+
+# Initialize and load Reconstruction Network
+model_recnet = NetworkReconstruction(layer_dims=layer_dims).to(device)
+checkpoint_recnet = torch.load(recnet_model_path, map_location=device, weights_only=False)
+model_recnet.load_state_dict(checkpoint_recnet)
+model_recnet.eval()
+
+print(f"Reconstruction Network loaded successfully from {cancer_type}")
+
+# ============================================================================
+# Use the models
+# ============================================================================
+
+# Example: Apply VAE to your data
+# your_data = torch.tensor(your_data_array).float().to(device)
+# with torch.no_grad():
+#     vae_output = model_vae(your_data)
+#     recnet_output = model_recnet(vae_output)
+
+```
+
+## Citation
+
+!!! warning "Warning"
+	This citation is temporary. It will be updated when a pre-print is released.
+
+If you use these pretrained models, please cite:
+
+```bibtex
+@software{renalprog2024,
+  title = {RenalProg: A Deep Learning Framework for Kidney Cancer Progression Modeling},
+  author = {[Guillermo Prol-Castelo, Elina Syrri, Nikolaos Manginas, Vasileos Manginas, Nikos Katzouris, Davide Cirillo, George Paliouras, Alfonso Valencia]},
+  year = {2025},
+  url = {https://github.com/gprolcas/renalprog},
+  note = {Preprint in preparation}
+}
+```
+
+## Training Details
+
+These models were trained using:
+- Random seed: 2023
+- Train/test split: 80/20
+- Optimizer: Adam
+- Learning rate: 1e-4
+- Batch size: 8
+- Beta annealing (for VAE): 3 cycles with 0.5 ratio
+
+## Model Performance
+
+**KIRC Model**:
+- Reconstruction loss (test): ~1.1
+
+**BRCA Model**:
+- Reconstruction loss (test): ~0.9
+
+## License
+
+These pretrained models are provided under the same Apache 2.0 license.
+
+## Contact
+
+For questions about the pretrained models, please:
+1. Check the [documentation](https://gprolcastelo.github.io/renalprog/)
+2. Open an issue on [GitHub](https://github.com/gprolcastelo/renalprog/issues)
+3. Contact the authors
+
+---
+
+**Last Updated**: December 2025
+**Version**: 1.0.0-alpha