Projects within

1. Protein Embeddings

Files:
- prot2vec.py
- tsne.py

Instructions:
 1. Run tsne.py to view embeddings
 
 2. If you use the reviewed-uniprot-lim_sequences.txt file
  Training times on 1 GPU per epoch ~ 1 hour and 30 minutes
  
  
If you want to run your own dataset:
 1. Change SequenceData dataset to your text file.
  a. line 1 = 'sequence'
  b. every line should be a protein sequence no seperator between amino acids
   i.e MGSKTLPAPVPIHPSLQLTNYSFLQAFNGL...\n
   
 2. Run prot2vec.py with your dataset
  a. Alter training parameters if need be
  
 3. Run tsne.py to view embeddings

2. Ground State Energy Prediction

 - Instructions:
   1. Download jsons: https://www.kaggle.com/datasets/burakhmmtgl/predict-molecular-properties
   2. Run feature_extraction.py, edit the structure such that you have a jsons folder in the data directory
   3. Run gse_prediction.py

3. Machine Interrogator Turing Test

 - The write up includes methods, data information, and purpose of project.
 - Within this project there are 3 models:
     - Word Embedding model (Skip-gram word2vec)
     - Machine Responder model (TextGenerate)
     - Machine Interrogator model (MachineInterrogator)
     
 - Used jokes dataset to train the interrogator on question responding. 
    The interrogator was then asked to interpret a human completion of the joke vs a machine completion of the joke.