In this repository you can find the code associated to the paper "The landscape of biomedical research" (https://www.biorxiv.org/content/10.1101/2023.04.10.536208v1).
The notebooks 01-15 contain the code to reproduce all the experiments and analyses performed in the paper. The notebooks 01-11 in the scripts/figure-scripts/ folder contain the code to generate the final figures included in the paper. All figures generated with the notebooks will be stored in the results/figures/ folder.
In order to be able to run 01-rgm-data-parse.ipynb, the dataset needs to be first downloaded into the data/ directory from https://www.nlm.nih.gov/databases/download/pubmed_medline.html and then unziped. PubMed releases a new snapshot of their database every year; they call it a "baseline". The data used in the paper is the 2020 baseline (download date: 26.01.2021, not available anymore) supplemented with additional files from the 2021 baseline (download date: 27.04.2022, not available anymore). However, any files from the currently available baseline could be used instead. For that the notebook 00-rgm-data-download.ipynb can be used (it should be noted that the path to the pubmed baseline should be changed accordingly to which baseline you want to download). Also, the FTP connection breaks after downloading several hundreds of files and one needs to reestablish the connection.
As one runs the notebooks, the computed variables and intermediate results will be stored in the results/variables/ directory. As the dataset and the obtained variables are too large, they are not included in the repository. However, you can find the t-SNE embedding along with some metadata in here: (https://zenodo.org/record/7695390).
This project depends on Python (pip install . in the project root, i.e.:
git clone https://github.com/berenslab/pubmed-landscape
cd pubmed-landscape
pip install -e .
Here there is a more detailed description on what you can find in the different notebooks in the scripts/ folder.
Notebooks in scripts/:
01-02: obtain and prepare the data.03-05: pipelines to transform the abstracts from raw text to 2D embedding, both BERT-based and TF-IDF ones.06-08: metrics calculations (kNN recall, kNN accuracy and isolatedness).09: analysis of Covid-19 (Section 2.1 from the paper).10: analysis of neurscience (Section 2.2 from the paper).11: analysis of machine learning (Section 2.3 from the paper).12-14: analysis of author's gender (Section 2.4 from the paper).15: whitening experiment (see Methods, section 4.3).
Notebooks and Python files in scripts/BERT-based-embeddings/ (Table 3, section 4.3 of the paper):
01-rgm-ls-malteos.ipynb: compute the SciNCL embeddings of the abstracts and the kNN accuracy of the representation.02-rgm-ls-SBERT.ipynb: compute the SBERT embeddings of the abstracts and the kNN accuracy of the representation.03-rgm-ls-PubMedBERT.ipynb: compute the PubMedBERT embeddings of the abstracts and the kNN accuracy of the representation.04-rgm-ls-BERT.ipynb: compute the BERT embeddings of the abstracts and the kNN accuracy of the representation.05-rgm-pipeline-TFIDF-1M.ipynb: compute the TF-IDF rerpesentation and its kNN accuracy of the corresponding 1M subset used for the BERT-based models comparison.06-rgm-pipeline-BERT-models.ipynb: compute the t-SNE embedding of the different BERT-based models representations.bert-models.py: compute the embeddings of the abstracts and the kNN accuracy for the rest of the BERT-based models.