CSCoV: Computational Studies about COVID-19

This repository includes source codes to build the CSCoV database. The latest build can be directly downloaded from Zenodo at https://zenodo.org/record/5136684.

The code includes two parts:

• Data collection and categorization (in R)
• ML models (in Python).

Data collection and categorization

• data_collection.R: collects papers from Pubmed, arXiv, bioRxiv, and medRxiv. Also gathers article and author-related information from Semantic Scholar.
• topic_modeling.R: analyzes articles to split them into topics.

Machine Learning models

ML models will use data from R scripts to provide an additional score for preprint papers.

Install dependencies:

pip install -r requirements.txt

Order to run scripts:

• 0.LDA_topics.ipynb: evaluate topic numbers of LDA models.
• 1.Doc2vec_embedding.ipynb: retrieve Doc2Vec embeddings.
• 2.DeepWalk_embeddings.ipynb: get DeepWalk embeddings from the citation network.
• 3.Add_pub_metric_info.ipynb: manually verify the labels.
• 4.Deep_Learning.ipynb: train DL models and predict probability.
• 5.Visualize_graph.ipynb: get citation edges, node degrees and LDA topics for citation graph visualizaiton.