[12] Patrizio Bellan, Han van der Aa, Mauro Dragoni, Chiara Ghidini, and Simone Paolo Ponzetto. PET: An Annotated Dataset for Process Extraction from Natural Language Text Tasks. In Cristina Cabanillas, Niels Frederik Garmann-Johnsen, and Agnes Koschmider, editors, Business Process Management Workshops, Lecture Notes in Business Information Processing, pages 315-321, Cham, 2023. Springer International Publishing. ISBN 978-3-031-25383-6. doi: 10.1007/978-3-031-25383-6_23.
[13] Abhirut Gupta, Abhay Khosla, Gautam Singh, and Gargi Dasgupta. Mining Procedures from Technical Support Documents, May 2018. URL http://arxiv.org/abs/1805.09780.arXiv:1805.09780 [cs].
[14] Nature Methods. Method of the Year 2013. Nature Methods, 11(1):1-1, January 2014. ISSN 1548-7105. doi: 10.1038/nmeth.2801. URL https://www.nature.com/articles/nmeth.2801. Number: 1 Publisher: Nature Publishing Group.
[15] James Eberwine, Jai-Yoon Sul, Tamas Bartfai, and Junhyong Kim. The promise of single-cell sequencing. Nature Methods, 11(1):25-27, January 2014. ISSN 1548-7105. doi: 10.1038/nmeth.2769. URL https://www.nature.com/articles/nmeth.2769. Number: 1 Publisher: Nature Publishing Group.
[16] Fuchou Tang, Catalin Barbacionu, Yangzhou Wang, Ellen Nordman, Clarence Lee, Nanlan Xu, Xiaohui Wang, John Bodeau, Brian B. Tuch, Asim Siddiqui, Kaiqin Lao, and M. Azim Surani. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods, 6(5):377-382, May 2009. ISSN 1548-7105. doi: 10.1038/nmeth.1315.
[17] Luke Zappia, Belinda Phipson, and Alicia Oshlack. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLOS Computational Biology, 14(6):e1006245, June 2018. ISSN 1553-7358. doi: 10.1371/journal.pcbi.1006245. URL https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006245. Publisher: Public Library of Science.
[18] Rahul Satija, Jeffrey A. Farrell, David Gennert, Alexander F. Schier, and Aviv Regev. Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5):495-502, May 2015. ISSN 1546-1696. doi: 10.1038/nbt.3192. URL https://www.nature.com/articles/nbt.3192. Number: 5 Publisher: Nature Publishing Group.
[19] Iz Beltagy, Kyle Lo, and Arman Cohan. SciBERT: A Pretrained Language Model for Scientific Text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3615-3620, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10.18653/v1/D19-1371. URL https://aclanthology.org/D19-1371.
[20] Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. ACM Transactions on Computing for Healthcare, 3 (1):2:1-2:23, October 2021. ISSN 2691-1957. doi: 10.1145/3458754. URL https://dl.acm.org/doi/10.1145/3458754.
[21] Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234-1240, February 2020. ISSN 1367-4803. doi: 10.1093/bioinformatics/btz682. URL https://doi.org/10.1093/bioinformatics/btz682.
[22] Yifan Peng, Shankai Yan, and Zhiyong Lu. Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 58-65, Florence, Italy, August 2019. Association for Computational Linguistics. doi: 10.18653/v1/W19-5006. URL https://aclanthology.org/W19-5006.