MOTIVATION: Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depend on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction (RE). RESULTS: In this article, we introduce the Relationship Extraction Module of the web-based application Mark2Cure (M2C) and demonstrate that citizen scientists can perform RE. We confirm the importance of accurate named entity recognition on user performance of RE and identify design issues that impacted data quality. We find that the data generated by citizen scientists can be used to identify relationship types not currently available in the M2C Relationship Extraction Module. We compare the citizen science-generated data with algorithm-mined data and identify ways in which the two approaches may complement one another. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration and natural language processing. AVAILABILITY AND IMPLEMENTATION: Mark2Cure platform: https://mark2cure.org; Mark2Cure source code: https://github.com/sulab/mark2cure; and data and analysis code for this article: https://github.com/gtsueng/M2C_rel_nb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depend on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction (RE). RESULTS: In this article, we introduce the Relationship Extraction Module of the web-based application Mark2Cure (M2C) and demonstrate that citizen scientists can perform RE. We confirm the importance of accurate named entity recognition on user performance of RE and identify design issues that impacted data quality. We find that the data generated by citizen scientists can be used to identify relationship types not currently available in the M2C Relationship Extraction Module. We compare the citizen science-generated data with algorithm-mined data and identify ways in which the two approaches may complement one another. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration and natural language processing. AVAILABILITY AND IMPLEMENTATION: Mark2Cure platform: https://mark2cure.org; Mark2Cure source code: https://github.com/sulab/mark2cure; and data and analysis code for this article: https://github.com/gtsueng/M2C_rel_nb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Juan Antonio Lossio-Ventura; William Hogan; François Modave; Yi Guo; Zhe He; Xi Yang; Hansi Zhang; Jiang Bian Journal: BMC Med Inform Decis Mak Date: 2018-07-23 Impact factor: 2.796
Authors: Francisco J Candido Dos Reis; Stuart Lynn; H Raza Ali; Diana Eccles; Andrew Hanby; Elena Provenzano; Carlos Caldas; William J Howat; Leigh-Anne McDuffus; Bin Liu; Frances Daley; Penny Coulson; Rupesh J Vyas; Leslie M Harris; Joanna M Owens; Amy F M Carton; Janette P McQuillan; Andy M Paterson; Zohra Hirji; Sarah K Christie; Amber R Holmes; Marjanka K Schmidt; Montserrat Garcia-Closas; Douglas F Easton; Manjeet K Bolla; Qin Wang; Javier Benitez; Roger L Milne; Arto Mannermaa; Fergus Couch; Peter Devilee; Robert A E M Tollenaar; Caroline Seynaeve; Angela Cox; Simon S Cross; Fiona M Blows; Joyce Sanders; Renate de Groot; Jonine Figueroa; Mark Sherman; Maartje Hooning; Hermann Brenner; Bernd Holleczek; Christa Stegmaier; Chris Lintott; Paul D P Pharoah Journal: EBioMedicine Date: 2015-05-09 Impact factor: 8.143
Authors: Ginger Tsueng; Julia L Mullen; Manar Alkuzweny; Marco Cano; Benjamin Rush; Emily Haag; Alaa Abdel Latif; Xinghua Zhou; Zhongchao Qian; Emory Hufbauer; Mark Zeller; Kristian G Andersen; Chunlei Wu; Andrew I Su; Karthik Gangavarapu; Laura D Hughes Journal: bioRxiv Date: 2022-06-02
Authors: Giulia Antonazzo; Jose M Urbano; Steven J Marygold; Gillian H Millburn; Nicholas H Brown Journal: Database (Oxford) Date: 2020-01-01 Impact factor: 3.451