Abstract
The volume of the biological literature has been increasing fast, which leads to a rapid growth of biological pathway figures included in the related biological papers. Each pathway figure encompasses rich biological information, consisting of gene names and gene relations. However, manual curations for pathway figures require tremendous time and labor. While leveraging advanced image understanding models may accelerate the process of curations, the accuracy of these models still needs improvements. Since each pathway figure is associated with a paper, most of the gene names and gene relations in a pathway figure also appear in the related paper text, where we can utilize text mining to improve the image recognition results. In this paper, we applied a fuzzy match method to detect gene names with different “gene dictionaries,” as well as gene co-occurrence in the plain text for suggesting gene relations. We have demonstrated that the performance of image understanding for both gene name recognitions and gene relation extractions can be improved with the help of text mining methods. All the data and code are available at GitHub (https://github.com/lyfer233/Text-Mining-Enhancements-for-Image-Recognition-of-Gene-Names-and-Gene-Relations).
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Acknowledgements
The authors would like to express their gratitude to Clement Essien, Drs. Richard Hammer and Dmitriy Shin for helpful discussions. The research is supported by the National Library of Medicine of the National Institute of Health (NIH) award 5R01LM013392.
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Ren, Y. et al. (2022). Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_11
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