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Author: rafaelperes

README.md

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+# TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in *in vitro* to *in vivo* settings
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+TUGDA is a novel multi-task unsupervised domain adaptation method that leverages transfer learning from tasks/domains in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared domain/task feature representations. TUGDA's ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of negative transfer and sucessfully transfer knowledge across biological models.
+
+**Model Representation:**
+![Image of Yaktocat](https://github.com/CSB5/TUGDA/blob/main/fig1_model.png)
+
+
+TUGDA framework for multi-task learning and domain adaptation in cancer drug response prediction. The layer L receives input data from different biological models and maps them to a latent space Z. Then, the multi-task layer S uses these latent features to make predictions, as well as compute task-uncertainties U t for regularizing the amount of transfer from tasks/domains in A to the latent features in Z by employing an autoencoder regularization. Using adversarial learning, the discriminator D receives the extracted features from Z and regularizes L to learn domain-invariant features. L, S, A and D consist of a single fully connected layer.
+
+# Usage examples
+
+We provide two notebooks as examples of how the training and testing is perfomed using TUGDA's framework. Both notebooks are self-contained (e.g., install required libs and load the necessary data). 
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+Step zero: bash setup_repo.sh
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+1) For MTL settings, please refer to [notebooks](https://github.com/CSB5/TUGDA/blob/main/tugda_mtl_example.ipynb). In this notebook you can reproduce TUGDA's result for Figure 2.
+
+2) For Domain Adaptation settings, please refer to [notebooks](https://github.com/CSB5/TUGDA/blob/main/tugda_da_example.ipynb). In this notebook, you can reproduce TUGDA's result for the domain adaptation from cell-lines to PDX (Figure 3).
+
+# Data
+In this repository we used data from the publicly available [GDSC](https://www.cancerrxgene.org/) and [PDX Novartis](https://www.nature.com/articles/nm.3954) datasets.
+
+# Citation
+Preprint to be released.
+
+# Contact information
+
+For additional information, help and bug reports please email Rafael Peres da Silva ([[email protected]](mailto:[email protected]))

data/GDSCDA_fpkm_AUC_all_drugs.zip

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+conda create --name TUGDA_rep python=3.8
+source activate TUGDA_rep
+pip install jupyter
+pip install ipykernel
+python -m ipykernel install --user --name TUGDA_rep --display-name "Python (TUGDA_rep)"
+jupyter notebook