Editor's introduction to the special section on the 7th Biomedical Linked Annotation Hackathon (BLAH7).

2021 Korea Genome Organization This is an open-access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The special section is dedicated to reporting achievements of the 7th Biomedical Linked Annotation Hackathon (BLAH7). BLAH is an annual hackathon event which is organized to join forces of biomedical text mining for the goal to promote interoperability among text mining resources. This year, the 7th edition was held in January, 2021. Due to the pandemic, it was organized as an online event, with the special theme “coronavirus disease 2019 (COVID-19)”. The goal was to develop text mining resources to help address the pandemic situation. During the hackathon, 47 participants from 11 countries worked on voluntarily organized projects, and the results are reported in this special collection. This section includes seven application notes and one opinion article. The first application note by Hernandez et al. [1] presents a Twitter dataset which includes more than 120 million “potentially clinically-relevant” tweets. The tweets are automatically annotated for clinically important named entities like drugs and symptoms. The dataset is released publicly to facilitate research on mining social media data for biomedical and clinical applications. Lithgow-Serrano et al. [2] presents named entity annotation of the LitCovid [3] dataset using OntoGene’s Biomedical Entity Recogniser (OGER) [4] and shows its effectiveness for document classification. Ouyang et al. [5] presents the AGAC annotation [6] added on top of the PubTator [7] and OGER annotations and shows that the addition is potentially useful to mine regulatory or causal relationships between biomedical entities. The following three papers represent efforts for multilingualism of text mining. Barros et al. [8] presents a multilingual parallel corpus of PubMed articles for the language pairs English-Portuguese and English-Spanish. Their corpus was annotated for biomedical entities and also relationships between them, which was then used to develop a multilingual recommendation dataset for recommending biomedical entities to the authors of the articles. Yamaguchi et al. [9] and Soares et al. [10] are written by the same set of authors. They developed two versions of Japanese translation of MeSH terms, one through merging of existing resources and manual curation, and another through an automatic translation method, of which the results are reported in the two separate application notes. Larmande et al. [11] reports a revision to OryzaGP [12], a corpus of PubMed articles relevant to rice species, which are automatically annotated for proteins and genes. The last one by Dohi et al. [13] presents the authors’ opinion after their case study with Alexander disease towards visualizing the phenotype diversity. Editor’s introduction to the special section on the 7th Biomedical Linked Annotation Hackathon (BLAH7) Jin-Dong Kim, Kevin Bretonnel Cohen, Fabio Rinaldi, Zhiyong Lu, Hyun-Seok Park Database Center for Life Science (DBCLS), Research Organization of Information and Systems (ROIS), Kashiwa, Chiba 277-0871, Japan School of Medicine, University of Colorado, Aurora, CO 80045, USA Dalle Molle Institute for Artificial Intelligence Research (IDSIA), 6928 Manno, Switzerland National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH), Bethesda, MD 20894, USA Center for Convergence Research of Advanced Technologies, Ewha Womans University, Seoul 03760, Korea Received: September 27, 2021 Accepted: September 27, 2021