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+Description
+-----------------
+
+- With this homework, we will practice building multiple models on the command-line. 
+- We will use multi-omics data from human cancer cell lines from the CCLE and GDSC databases. 
+- We will build the models on the CCLE dataset and evaluate the models on the GDSC dataset. 
+- We will do a benchmarking of different deep learning architectures and different combinations of omic data modalities used as input.
+- Finally, we will explore the best performing models in a jupyter notebook. 
+
+Steps for the homework:
+
+  1. Download and unpack the data: https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/ccle_vs_gdsc.tgz
+    
+  2. Familiarize yourself with the command-line options for Flexynesis:
+    See tutorial here: https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis/site/getting_started/
+
+  3. Use flexynesis on the command-line to predict drug responses for “Erlotinib”.
+    Write a bash script to run the following experiments: 
+      Try a combination of:
+     
+      a) **different architectures**: e.g. DirectPred, Supervised VAE, GNN (Test at least 2 of these). 
+     
+      b) **data type combinations** (e.g. mutation, mutation + rna, mutation + cnv) (Test at least 2 of these) 
+     
+      c) **fusion methods**: early, intermediate (applies only to tools other than GNN)
+      
+      So, in total, you will run maximally 3 x 3 x 2 = 18 different flexynesis runs (and minimally 2 x 2 x 2 = 8 different runs).
+
+      **Note**: GNNs actually only support "early" fusion, so you can skip "intermediate" fusion for GNNs, but you can try different graph convolution options for GNNs.
+        For GNNs, try "GC" and "SAGE" as different options in your experiment (See --gnn_conv_type argument). 
+
+      **Hint 1**: Restrict your analysis to 5-10% of the features (use a combination of variance and laplacian score filtering).
+     
+      **Hint 2**: It is okay to use few HPO iterations for this exercise (e.g. 15 iterations or so) (considering the resourse/time limits etc). 
+       The point of this exercise is not to find the perfect model, but to get an insight on the idea of benchmarking different setups. 
+     
+  5. Open a jupyter notebook and do the following:
+  
+        a) Import the results of the experiments from step 3, and rank the experiments based on performance (pearson_corr)
+        Which combination yields the best results?
+      
+        b) Explore the train/test embeddings from the best model (from 4a).
+     
+        c) Import the feature importance scores from the best model (from 4a). 
+           Get top 10 markers. Do literature search. Are any of the top markers associated to “Erlotinib”?  
+
+
+  6. Submit the jupyter notebook from step 4 as your assignment on the google classroom. 
+