Jin-Dong Kim, K. Cohen, Nigel Collier, Zhiyong Lu, Fabio Rinaldi
{"title":"Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining","authors":"Jin-Dong Kim, K. Cohen, Nigel Collier, Zhiyong Lu, Fabio Rinaldi","doi":"10.5808/GI.2019.17.2.e12","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e12","url":null,"abstract":"2019, 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. Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining Jin-Dong Kim, Kevin Bretonnel Cohen, Nigel Collier, Zhiyong Lu, Fabio Rinaldi Database Center for Life Science, Research Organization of Information and Systems, Kashiwa 277-0871, Japan School of Medicine, University of Colorado, Aurora, CO 80045, USA Faculty of Modern and Medieval Languages, University of Cambridge, Cambridge CB3 9DP, UK National Center for Biotechnology Information (NCBI), U.S. National Library of Medicine (NLM), Bethesda, MD 20894, USA Institute of Computational Linguistics, University of Zurich, Zurich CH-8050, Switzerland IDSIA, Manno CH-6928, Switzerland Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49042054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"OryzaGP: rice gene and protein dataset for named-entity recognition","authors":"P. Larmande, Huy Do, Yue Wang","doi":"10.5808/GI.2019.17.2.e17","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e17","url":null,"abstract":"Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43083551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fully connecting the Observational Health Data Science and Informatics (OHDSI) initiative with the world of linked open data","authors":"J. Banda","doi":"10.5808/GI.2019.17.2.e13","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e13","url":null,"abstract":"The usage of controlled biomedical vocabularies is the cornerstone that enables seamless interoperability when using a common data model across multiple data sites. The Observational Health Data Science and Informatics (OHDSI) initiative combines over 100 controlled vocabularies into its own. However, the OHDSI vocabulary is limited in the sense that it combines multiple terminologies and does not provide a direct way to link them outside of their own self-contained scope. This issue makes the tasks of enriching feature sets by using external resources extremely difficult. In order to address these shortcomings, we have created a linked data version of the OHDSI vocabulary, connecting it with already established linked resources like bioportal, bio2rdf, etc. with the ultimate purpose of enabling the interoperability of resources previously foreign to the OHDSI universe.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43234180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. García, Olga X. Giraldo, A. Garcia, D. Rebholz-Schuhmann
{"title":"Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications","authors":"L. García, Olga X. Giraldo, A. Garcia, D. Rebholz-Schuhmann","doi":"10.5808/GI.2019.17.2.e14","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e14","url":null,"abstract":"The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results. Bioschemas is based on the standards of schema.org, providing new types, properties and guidelines for metadata, i.e., providing metadata profiles tailored to the Life Sciences domain. Here we present our proposed contribution to Bioschemas (from the project “Biotea”), which supports metadata contributions for scholarly publications via profiles and web components. Biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our Biotea model has been mapped to schema.org following Bioschemas standards.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41759719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}