move from private repo

Author: kexinhuang12345

README.md

@@ -1 +1,121 @@
-# TxGNN
+# TxGNN: Repurposing therapeutics for neglected diseases using geometric deep learning
+
+This repository hosts the official implementation of TxGNN, a method that can predict drug efficacy to disease with limited molecular underpinnings and few treatments by applying geomtric learning on multi-scale disease knowledge graph. 
+
+### Installation 
+
+Create your virtual environment using `virtualenv` or `conda` and then do `pip install TxGNN`
+
+### Core API Interface
+Using the API, you can (1) reproduce the results in our paper and (2) train TxGNN on your own drug repurposing dataset using a few lines of code, and also generate graph explanations. 
+
+```python
+from TxGNN import TxData, TxGNN, TxEval
+
+# Download/load knowledge graph dataset
+TxData = TxData(data_folder_path = './data')
+TxData.prepare_split(split = 'complex_disease', seed = 42)
+TxGNN = TxGNN(data = TxData, 
+              weight_bias_track = False,
+              proj_name = 'TxGNN',
+              exp_name = 'TxGNN'
+              )
+
+# Initialize a new model
+TxGNN.model_initialize(n_hid = 100, 
+                      n_inp = 100, 
+                      n_out = 100, 
+                      proto = True,
+                      proto_num = 3,
+                      attention = False,
+                      sim_measure = 'all_nodes_profile',
+                      bert_measure = 'disease_name',
+                      agg_measure = 'rarity',
+                      num_walks = 200,
+                      walk_mode = 'bit',
+                      path_length = 2)
+
+```
+
+Instead of initializing a new model, you can also load a saved model:
+
+```python
+TxGNN.load_pretrained('./model_ckpt')
+```
+
+To do pre-training using link prediction for all edge types, you can type:
+
+```python
+TxGNN.pretrain(n_epoch = 2, 
+               learning_rate = 1e-3,
+               batch_size = 1024, 
+               train_print_per_n = 20)
+```
+
+Lastly, to do finetuning on drug-disease relation with metric learning, you can type:
+
+```python
+TxGNN.finetune(n_epoch = 500, 
+               learning_rate = 5e-4,
+               train_print_per_n = 5,
+               valid_per_n = 20,
+               save_name = finetune_result_path)
+```
+
+To save the trained model, you can type:
+
+```python
+TxGNN.save_model('./model_ckpt')
+```
+
+To evaluate the model on the entire test set using disease-centric evaluation, you can type:
+
+```python
+result = TxEval.eval_disease_centric(disease_idxs = 'test_set', 
+                                     show_plot = False, 
+                                     verbose = True, 
+                                     save_result = True,
+                                     return_raw = False,
+                                     save_name = 'SAVE_PATH')
+
+```
+
+If you want to look at specific disease, you can also do:
+
+```python
+result = TxEval.eval_disease_centric(disease_idxs = [9907.0, 12787.0], 
+                                     relation = 'indication', 
+                                     save_result = False)
+```
+
+
+After training a satisfying link prediction model, we can also train graph XAI model by:
+
+```python
+TxGNN.train_graphmask(relation = 'indication',
+                      learning_rate = 3e-4,
+                      allowance = 0.005,
+                      epochs_per_layer = 3,
+                      penalty_scaling = 1,
+                      valid_per_n = 20)
+```
+
+You can retrieve and save the graph XAI gates (whether or not an edge is important) into a pkl file located as `SAVED_PATH/'graphmask_output_RELATION.pkl'`:
+
+```python
+gates = TxGNN.retrieve_save_gates('SAVED_PATH')
+```
+
+Of course, you can save and load graphmask model as well via:
+
+```python
+TxGNN.save_graphmask_model('./graphmask_model_ckpt')
+TxGNN.load_pretrained_graphmask('./graphmask_model_ckpt')
+
+```
+
+
+### Cite Us
+
+```
+```