The gene normalization task in BioCreative III
We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected...
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| Published in | BMC bioinformatics Vol. 12 Suppl 8; no. S8; p. S2 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central
03.10.2011
BioMed Central Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).
We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively.
By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance. |
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| AbstractList | BACKGROUND: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). RESULTS: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. CONCLUSIONS: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance. We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance. Abstract Background: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k ). Results: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k =5), 0.3538 (k =10), and 0.3535 (k =20), respectively. Higher TAP-k scores of 0.4916 (k =5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k =5), 0.4311 (k =10), and 0.4477 (k =20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. Conclusions: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance. BACKGROUNDWe report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).RESULTSWe received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively.CONCLUSIONSBy using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance. |
| ArticleNumber | S2 |
| Author | Gerner, Martin Verspoor, Karin Romacker, Martin Ruch, Patrick Wei, Chih-Hsuan Liu, Jingchen Rinaldi, Fabio Kuo, Cheng-Ju Okazaki, Naoaki Srinivasan, Padmini Bhattacharya, Sanmitra Lu, Zhiyong Liu, Hongfang Kao, Hung-Yu Dai, Hong-Jie Liu, Feifan Vishnyakova, Dina Hsu, Chun-Nan Tsai, Richard Tzong-Han Wilbur, W John Agarwal, Shashank Huang, Minlie Campos, David Cho, Han-Cheol Matos, Sergio Solt, Illes Livingston, Kevin M Torii, Manabu |
| AuthorAffiliation | 1 National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland 20894, USA 12 Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, 1117 Budapest, Hungary 7 Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, R.O.C 14 BiTem Group, Division of Medical Information Sciences, University of Geneva, Switzerland 16 NITAS/TMS, Text Mining Services, Novartis AG, Switzerland 13 Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA 3 Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China 9 Interfaculty Initiative in Information Studies, University of Tokyo, Japan 10 Graduate School of Information Science and Technology, University of Tokyo, Japan 17 Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland 4 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan 20 Lab of Text Intelligence in Biom |
| AuthorAffiliation_xml | – name: 2 Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C – name: 9 Interfaculty Initiative in Information Studies, University of Tokyo, Japan – name: 8 Institute of Information Science, Academic Sinica, Taipei, Taiwan, R.O.C – name: 22 Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado, USA – name: 14 BiTem Group, Division of Medical Information Sciences, University of Geneva, Switzerland – name: 3 Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China – name: 4 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan – name: 6 Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C – name: 10 Graduate School of Information Science and Technology, University of Tokyo, Japan – name: 18 Department of Computer Science, The University of Iowa, Iowa City, Iowa 52242, USA – name: 12 Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, 1117 Budapest, Hungary – name: 17 Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland – name: 5 Information Science Institute, University of Southern California, Marina del Rey, California, USA – name: 16 NITAS/TMS, Text Mining Services, Novartis AG, Switzerland – name: 11 Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK – name: 20 Lab of Text Intelligence in Biomedicine, Georgetown University Medical Center, 4000 Reservoir Rd., NW, Washington, DC 20057 USA – name: 21 DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal – name: 19 Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905 USA – name: 7 Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, R.O.C – name: 13 Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA – name: 1 National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland 20894, USA – name: 15 BiTeM Group, Information Science Department, University of Applied Science, Geneva, Switzerland |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22151901$$D View this record in MEDLINE/PubMed |
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| Copyright | 2011 Lu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2011 Lu et al; licensee BioMed Central Ltd. 2011 Lu et al; licensee BioMed Central Ltd. |
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| Snippet | We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes... Abstract Background: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of... BACKGROUNDWe report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the... BACKGROUND: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of... |
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| Title | The gene normalization task in BioCreative III |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/22151901 https://www.proquest.com/docview/901923181 https://search.proquest.com/docview/912428915 http://dx.doi.org/10.1186/1471-2105-12-S8-S2 https://pubmed.ncbi.nlm.nih.gov/PMC3269937 |
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