Update README.md

Author: nabingiri

README.md

@@ -234,12 +234,12 @@ python3 validation/validate_mrc.py
 
 
 ## Programs to validate the dataset using deep learning
-To validate the utility and quality of Cryo2StructData, we designed two deep transformer models and trained and test them on Cryo2StructData to predict backbone atoms and amino acid types from density maps.
+To validate the utility and quality of Cryo2StructData, we designed two deep transformer models and trained and tested them on Cryo2StructData to predict backbone atoms and amino acid types from density maps. We have released the source code for training and validation of these deep learning models, which is available here: https://github.com/jianlin-cheng/Cryo2Struct . The paper describing the method in detail is available here: https://doi.org/10.1101/2024.01.02.573943 .
 
 ### Deep transformer to predict protein backbone atoms and amino acid types
 The inference program for the deep transformer is available in [prediction/src/infer/](prediction/src/infer/). Download the model checkpoints from [Trained on Full Cryo2StructData](https://doi.org/10.7910/DVN/SXNYRE) or [Trained on Small Subset Cryo2StructData](https://doi.org/10.7910/DVN/DTV4JF) and keep them in [prediction/checkpoints](prediction/checkpoints/) directory.
 
-### Hidden Markov Model (HMM) to link predicted Ca atoms into backbone structures
+### Hidden Markov Model (HMM) to link predicted carbon-alpha atoms into backbone structures
 The Hidden Markov Model-Guided carbon-alpha atom connection program are available in [prediction/src/viterbi/](prediction/src/viterbi/). The viterbi algorithm is written in C++ program, so compile them using:
 ```
 g++ -fPIC -shared -o viterbi.so viterbi.cpp -O3