Mao Yuqing, Van Auken Kimberly, Li Donghui, Arighi Cecilia N, McQuilton Peter, Hayman G Thomas, Tweedie Susan, Schaeffer Mary L, Laulederkind Stanley J F, Wang Shur-Jen, Gobeill Julien, Ruch Patrick, Luu Anh Tuan, Kim Jung-Jae, Chiang Jung-Hsien, Chen Yu-De, Yang Chia-Jung, Liu Hongfang, Zhu Dongqing, Li Yanpeng, Yu Hong, Emadzadeh Ehsan, Gonzalez Graciela, Chen Jian-Ming, Dai Hong-Jie, Lu Zhiyong
National Center for Biotechnology Information (NCBI), National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20817, USA WormBase, Division of Biology, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125, USA, TAIR, Department of Plant Biology, The Arabidopsis Information Resource, Carnegie Institution for Science, Stanford, CA 94305, USA, Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA, FlyBase, Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK, Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA, USDA-ARS Plant Genetics Research Unit and Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA, HES-SO, HEG, Library and Information Sciences, 7 route de Drize, CH-1227 Carouge, Switzerland, SIBtex, Swiss Institute of Bioinformatics, Rue Michel Servet 1, 1211 Geneva 4, Switzerland, School of Computer Engineering, Nanyang Technological University, Block N4, #02a-32, Nanyang Avenue, Singapore 639798, Department of Computer Science and Information Engineering, National Cheng-Kung University, No. 1, University Rd., Tainan 701, Taiwan, Republic of China, Department of Radiology, Mackay Memorial Hospital, Taitung Branch, Lane 303 Chang Sha St. Taitung, Taiwan, Republic of China, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA, Department of Computer Science, University of Delaware, 101 Smith Hall, Newark, DE 19716, USA, Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 Lake Avenue North (AC7-059), Worcester, MA 01655 USA, Department of Biomedical Informatics, Arizona State University, 13212 East Shea Boulevard Scottsdale, AZ 85259 USA, Institute of Information Science, Academia Sinica, 128 Academia Road, Secti
Database (Oxford). 2014 Aug 25;2014. doi: 10.1093/database/bau086. Print 2014.
UNLABELLED: Gene ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. DATABASE URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.
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