RelCurator: a text mining-based curation system for extracting gene–phenotype relationships specific to neurodegenerative disorders

• Published in: Genes & genomics 2023, 45(8), , pp.1025-1036
• Main Authors: Lee, Heonwoo, Jeon, Junbeom, Jung, Dawoon, Won, Jung-Im, Kim, Kiyong, Kim, Yun Joong, Yoon, Jeehee
• Format: Article
• Language: English
• Subjects: Analysis; Animal Genetics and Genomics; Anopheles; Biomedical and Life Sciences; Browsing; Computational linguistics; Data mining; Data Mining - methods; Deep learning; Degeneration; Genes; Genotype & phenotype; Human Genetics; Humans; Language processing; Life Sciences; Medical genetics; Microbial Genetics and Genomics; Natural language interfaces; Nervous system; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neurodegenerative Diseases - genetics; phenotype; Phenotypes; Plant Genetics and Genomics; Precision medicine; Prediction models; Research Article; support vector machines; user interface; 생물학
• ISSN: 1976-9571; 2092-9293; 2092-9293
• DOI: 10.1007/s13258-023-01405-6

Background The identification of gene–phenotype relationships is important in medical genetics as it serves as a basis for precision medicine. However, most of the gene-phenotype relationship data are buried in the biomedical literature in textual form. Objective We propose RelCurator, a curation system that extracts sentences including both gene and phenotype entities related to specific disease categories from PubMed articles, provides rich additional information such as entity taggings, and predictions of gene–phenotype relationships. Methods We targeted neurodegenerative disorders and developed a deep learning model using Bidirectional Gated Recurrent Unit (BiGRU) networks and BioWordVec word embeddings for predicting gene–phenotype relationships from biomedical texts. The prediction model is trained with more than 130,000 labeled PubMed sentences including gene and phenotype entities, which are related to or unrelated to neurodegenerative disorders. Results We compared the performance of our deep learning model with those of Bidirectional Encoder Representations from Transformers (BERT), Support Vector Machine (SVM), and simple Recurrent Neural Network (simple RNN) models. Our model performed better with an F1-score of 0.96. Furthermore, the evaluation done using a few curation cases in the real scenario showed the effectiveness of our work. Therefore, we conclude that RelCurator can identify not only new causative genes, but also new genes associated with neurodegenerative disorders’ phenotype. Conclusion RelCurator is a user-friendly method for accessing deep learning-based supporting information and a concise web interface to assist curators while browsing the PubMed articles. Our curation process represents an important and broadly applicable improvement to the state of the art for the curation of gene–phenotype relationships.