Towards Automated Meta-analysis of Biomedical Texts in the Field of Cell-based Immunotherapy
Keywords:cancer; cell-based immunotherapy; text mining; automated meta-analysis
Cell-based immunotherapy is a promising approach for the treatment of chronic infections, autoimmune disorders, and malignant tumors. There are many strategies of cell-based immunotherapy of cancer; these include injection of various immune effector cells, propagated and «trained» in a cell culture. Alternatively, cells presenting tumor antigens on their surface in a form recognized by the immune system can be used to achieve a therapeutic effect. The research results in this field are presented in thousands of texts, and their manual analysis is very complicated. We have developed an approach for automated text analysis in this area of biomedical science. Here we present the first results of the automated analysis of the data extracted from abstracts of scientific articles available in PubMed. These results demonstrate the associations between types of tumors and the most commonly used methods of their cell-based immunotherapy.
- Palucka, K., & Banchereau, J. (2013). Dendritic-cell-based therapeutic cancer vaccines. Immunity, 39(1), 38-48. DOI
- Lupatov, A. Yu., Karalkin, P. A., Boyko, A. A., & Yarygin, K. N. (2018). Autotransplantation of T-lymphocytes as a tool for antigen-specific immunotherapy of oncological diseases. Vestnik Transplantologii i Iskusstvennykh Organov, 20(3), 95-104. DOI
- Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017). Information retrieval and text mining technologies for chemistry. Chemicalreviews, 117(12), 7673-7761. DOI
- Tsuruoka, Y., Tateishi, Y., Kim, J.-D., Ohta, T., McNaught, J., Ananiadou, S., & Tsujii, J. (2005). Developing a robust part-of-speech tagger for biomedical text. Advances in Informatics, 3746, 382−392.>/li>
- Miyao, Y., & Tsujii, J. (2008). Feature forest models for probabilistic HPSG parsing. computational linguistics, 34(1), 35−80. DOI
- Hina, S., Atwell, E., & Johnson, O. (2010). Secure information extraction from clinical documents using snomed ct gazetteer and natural language processing . International conference for internet technology and secured transactions. IEEE, 1-5.
- Aronson, A. R., & Lang, F. M. (2010). An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3), 229–236. DOI
- Jagannatha, A. N., & Yu, H. (2016). Structured prediction models for RNN based sequence labeling in clinical text. Proceedings of the conference on empirical methods in natural language processing. NIH Public Access, 2016, 856–865.
- Mika, S., & Rost, B. (2004). Protein names precisely peeled off free text. Bioinformatics, 20(1), i241−i247. DOI
- McDonald, R., & Pereira, F. (2005). Identifying gene and protein mentions in text using conditional random fields. BMC Bioinformatics, 6(1), S6. DOI
- Zeng, D., Sun, D., Lin, L., & Liu, B. (2017). LSTM-CRF for drug-named entity recognition. Entropy, 19 (6), 283. DOI
- Wang, Y., Liu, S., Afzal, N., Rastegar-Mojarad, M., Wang, L., Shen, F., Kingsbury, P., & Liu, H. (2018). A comparison of word embeddings for the biomedical natural language processing. Journal of Biomedical Informatics, 87, 12-20. DOI
- Shelmanov, A. O., Smirnov, I. V., & Vishneva, E. A. (2015). Information extraction from clinical texts in Russian. Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue”, 14(21), 537-549.
- Yadav, M., & Goyal, N. (2015). Comparison of open source crawlers – a review. International Journal of Scientific and Engineering Research, 2229(5518), 1544-1551.
- Larionov, D., Shelmanov, A., Chistova, E., & Smirnov, I. (2019). Semantic role labeling with pretrained language models for known and unknown predicates. Proceedings of Recent Advances of Natural Language Processing, 620-630. https://github.com/IINemo/isanlp
- Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th Very Large Data Base Conference, 487-499.
- Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390. DOI
- Rosenberg, S. A., Yang, J. C., Sherry, R. M., Kammula, U. S., Hughes, M. S., Phan, G. Q., & Dudley M. E. (2011). Durable complete responses in heavily pretreated patients with metastatic melanoma using T-cell transfer immunotherapy. Clinical Cancer Research, 17(13), 4550-4557. DOI
- Radvanyi, L.G., Bernatchez, C., Zhang, M., Fox, P.S., Miller, P., Chacon, J., & Hwu, P. (2012). Specific lymphocyte subsets predict response to adoptive cell therapy using expanded autologous tumor-infiltrating lymphocytes in metastatic melanoma patients. Clinical Cancer Research, 18(24), 6758-6770. DOI
- Kochenderfer, J. N., Wilson, W. H., Janik, J. E., Dudley, M. E., Stetler-Stevenson, M., Feldman, S. A, & Rosenberg, S. A. (2010). Eradication of B-lineage cells and regression of lymphoma in a patient treated with autologous T cells genetically engineered to recognize CD19. Blood, 116(20), 4099-4102. DOI
- Flach, P. (2012). Machine learning: the art and science of algorithms that make sense of data. Book, Cambridge University Press.
- Lin, C., Miller, T., Dligach, D., Bethard, S., & Savova, G. (2019) A BERT-based universal model for both within-and cross-sentence clinical temporal relation extraction. Proceedings of the 2nd Clinical Natural Language Processing Workshop, 65-71. https://www.aclweb.org/ anthology/W19-1908
- Pang, N., Qianm L., Lyu, W., & Yang, J-D. (2019) Transfer learning for scientific data chain extraction in small chemical corpus with BERT-CRF model. arXiv preprint arXiv:1905.05615
- Hakala, K., Kaewphan, S., Salakoski, T., & Ginter, F. (2016) Syntactic analyses and named entity recognition for PubMed and PubMed Central—up-to-the-minute. Proceedings of the 15th Workshop on Biomedical Natural Language Processing, 102-107.
- Lupatov, A. Y., Yarygin, K. N., Panov, A. I., Suvorov, R. E., Shvets, A. V., Volkova, G. D. (2015). Assessment of dendritic cell therapy effectiveness based on the feature extraction from scientific publications. Proceedings of the International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2, 270-276. DOI
- Boyko, A. A., Kaidina, A. M., Kim, Y. C., Lupatov, A. Yu., Panov, A. I., Suvorov, R. E., Shvets, A. V. (2016). A framework for automated meta-analysis: dendritic cell therapy case study. 8th International Conference on Intelligent Systems (IEEE), 8, 160-166. DOI