Pub Date : 2019-06-01Epub Date: 2019-06-27DOI: 10.5808/GI.2019.17.2.e18
Mina Gachloo, Yuxing Wang, Jingbo Xia
Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.
{"title":"A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.","authors":"Mina Gachloo, Yuxing Wang, Jingbo Xia","doi":"10.5808/GI.2019.17.2.e18","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e18","url":null,"abstract":"<p><p>Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"17 2","pages":"e18"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6808632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.5808/GI.2019.17.2.e20
Arnaud Ferré, Mouhamadou Ba, Robert Bossy
Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.
实体规范化，或通用领域中的实体链接，是一项信息提取任务，旨在用语义引用（如本体的概念）注释/绑定原始文本中的多个单词/表达式。本体至少由一个正式组织的词汇表或术语层次结构组成，它捕获了一个领域的知识。目前，机器学习方法通常与分布式表示相结合，可以获得良好的性能。然而，这些需要大型训练数据集，而这些数据集并不总是可用的，尤其是对于专业领域的任务。CONTES（CONcept TErm System）是一种有监督的方法，它使用小型训练数据集来处理实体规范化和本体概念。CONTES有一些局限性，比如它不能很好地与非常大的本体相适应，它倾向于过度概括预测，并且它缺乏对词汇表外单词的有效表示。在这里，我们建议评估不同的方法来降低本体表示的维度。我们还建议校准参数，以使预测更准确，并用特定的方法解决词汇表外单词的问题。
{"title":"Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data","authors":"Arnaud Ferré, Mouhamadou Ba, Robert Bossy","doi":"10.5808/GI.2019.17.2.e20","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e20","url":null,"abstract":"Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.","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":"47317944","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}
Pub Date : 2019-06-01DOI: 10.5808/GI.2019.17.2.e12
Jin-Dong Kim, K. Cohen, Nigel Collier, Zhiyong Lu, Fabio Rinaldi
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
{"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}
Pub Date : 2019-06-01DOI: 10.5808/GI.2019.17.2.e17
P. Larmande, Huy Do, Yue Wang
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.
{"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}
Pub Date : 2019-06-01DOI: 10.5808/GI.2019.17.2.e13
J. Banda
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.
{"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}
Pub Date : 2019-06-01DOI: 10.5808/GI.2019.17.2.e14
L. García, Olga X. Giraldo, A. Garcia, D. Rebholz-Schuhmann
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.
{"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}
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