Extract genomic data from papers.

Made during GSoC 21 for WormBase

Setup:

1. Installation

pip3 install wheel   
pip3 install -r requirements.txt   

2. You'll need to put your wbtools credentials and TextPresso token inside utils\all_config.cfg

Steps:

Follow the notebooks serially.

1. Preparing data.ipynb: Download and prepare data for NER.
2. Hybrid extraction.ipynb: Train NER model, run hybrid mutation extraction on C. Elegans papers by listing their WBPaper ID.
3. Validation.ipynb: Normalize mutations and genes, get matches using the sequence file and cross-check final output if paper was already manually curated.

Results:

In 100 papers tested (93 were in the manually curated ground truth file), gene-mutation matches were found in 53 papers.
Total 2433 matches were present in those 53 papers. And 977 matches were found using this developed pipeline.
TP: 472, FP: 505
Precision: 48.3%
After manually checking the false positives and updating the ground truth file -
TP: 807, FP: 170
Precision: 82.59%
Not all FP are FP. After manual verification of the final output, some were noticed to be true positive which were originally missed during the manual curation.

Where Next

These ideas, while interesting, were not possible during the two-month coding period. If worked on, they might improve recall by a huge margin.

Majority of the extracted mutations from notebook 2 are being ignored (almost 73% of outputs during development). This is partly due to limitation in the mutation normalization block in notebook 3 and the subpar predictions from NER due to being trained on limited mutation data in natural language form.

1. Active learning to improve the NER model: Annotating from scratch could be very expensive, so using the existing data and the regex outputs to kick start the process, and manually verifing a few subsets of annotations with help of curators would be useful.
2. Better mutation normalization: Sounds very vague because it is. Current approach normalizes the easiest protein mutations. This will require a bit of research, however, improved NER outputs would certainly aid a lot.

More additional data which will help curators in final verification.

1. More data: This will help in easier and quicker verification, and can also be used to fill additional details in the ace file.
2. Smarter matching: A more complex gene-mutation matching will help in filtering out even more false positives. Especially useful when curators come across patterns that were previously unnoticed during the verification.

Faster and leaner.

1. Faster extraction: Mutation extraction in notebook 2 takes a tremendously long time - almost >1 hour for a single paper. This is mainly due to the big number of regular expressions used and while they can't be switched off, there might be a smarter way of doing it.
2. Leaner storage: Some of the data structure choices made in this project are a little questionable. While they work fine, it can be better.

Mentors

This project would not have been possible without their support.
Magdalena Zarowiecki
Paul Davis
Valerio Arnaboldi

Citation

@article{https://doi.org/10.17912/micropub.biology.000578,
doi = {10.17912/MICROPUB.BIOLOGY.000578},
url = {https://www.micropublication.org/journals/biology/micropub-biology-000578},
author = {Mallick, Rishab and Arnaboldi, Valerio and Davis, Paul and Diamantakis, Stavros and Zarowiecki, Magdalena and Howe, Kevin},
title = {Accelerated variant curation from scientific literature using biomedical text mining},
publisher = {microPublication Biology},
year = {2022}
}