MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning

Gupta, S.; Rao, S.; Miglani, T.; Iyer, Y.; Lin, J.; Saiyed, A. M.; Ikwuemesi, I.; McNulty, S.; Thaxton, C.; Madhavan, S.

doi:10.1101/2020.12.03.409094

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bioRxiv · 10.1101/2020.12.03.409094

MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning

Gupta, S.·Rao, S.·Miglani, T.·Iyer, Y.·Lin, J.·Saiyed, A. M.·Ikwuemesi, I.·McNulty, S.·Thaxton, C.·Madhavan, S.

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Abstract

Interpretation of a given variants pathogenicity is one of the most profound challenges to realizing the promise of genomic medicine. A large amount of information about associations between variants and diseases used by curators and researchers for interpreting variant pathogenicity is buried in biomedical literature. The development of text-mining tools that can extract relevant information from the literature will speed up and assist the variant interpretation curation process. In this work, we present a text-mining tool, MACE2k that extracts evidence sentences containing associations between variants and diseases from full-length PMC Open Access articles. We use different machine learning models (classical and deep learning) to identify evidence sentences with variant-disease associations. Evaluation shows promising results with the best F1-score of 82.9% and AUC-ROC of 73.9%. Classical ML models had a better recall (96.6% for Random Forest) compared to deep learning models. The deep learning model, Convolutional Neural Network had the best precision (75.6%), which is essential for any curation task.

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Gupta, S., Rao, S., Miglani, T., Iyer, Y., Lin, J., Saiyed, A. M., Ikwuemesi, I., McNulty, S., Thaxton, C., Madhavan, S.. 2020-12-04. MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning. https://doi.org/10.1101/2020.12.03.409094

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