Xi Yang1, Zhuo Song2, Chengkun Wu3,4, Wei Wang1, Gen Li2, Wei Zhang2, Lingqian Wu5, Kai Lu6. 1. School of Computer Science, National University of Defense Technology, Changsha, 410073, China. 2. Genetalks Biotech Inc., Beijing, 100176, China. 3. School of Computer Science, National University of Defense Technology, Changsha, 410073, China. chengkun_wu@nudt.edu.cn. 4. Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, 410073, China. chengkun_wu@nudt.edu.cn. 5. Center for Medical Genetics, Central South University, 110 Xiangya Road, Changsha, 410078, Hunan, China. wulingqian@sklmg.edu.cn. 6. School of Computer Science, National University of Defense Technology, Changsha, 410073, China. kailu@nudt.edu.cn.
Abstract
BACKGROUND: The detection and interpretation of CNVs are of clinical importance in genetic testing. Several databases and web services are already being used by clinical geneticists to interpret the medical relevance of identified CNVs in patients. However, geneticists or physicians would like to obtain the original literature context for more detailed information, especially for rare CNVs that were not included in databases. RESULTS: The resulting CNVdigest database includes 440,485 sentences for CNV-disease relationship. A total number of 1582 CNVs and 2425 diseases are involved. Sentences describing CNV-disease correlations are indexed in CNVdigest, with CNV mentions and disease mentions annotated. CONCLUSIONS: In this paper, we use a systematic text mining method to construct a database for the relationship between CNVs and diseases. Based on that, we also developed a concise front-end to facilitate the analysis of CNV/disease association, providing a user-friendly web interface for convenient queries. The resulting system is publically available at http://cnv.gtxlab.com /.
BACKGROUND: The detection and interpretation of CNVs are of clinical importance in genetic testing. Several databases and web services are already being used by clinical geneticists to interpret the medical relevance of identified CNVs in patients. However, geneticists or physicians would like to obtain the original literature context for more detailed information, especially for rare CNVs that were not included in databases. RESULTS: The resulting CNVdigest database includes 440,485 sentences for CNV-disease relationship. A total number of 1582 CNVs and 2425 diseases are involved. Sentences describing CNV-disease correlations are indexed in CNVdigest, with CNV mentions and disease mentions annotated. CONCLUSIONS: In this paper, we use a systematic text mining method to construct a database for the relationship between CNVs and diseases. Based on that, we also developed a concise front-end to facilitate the analysis of CNV/disease association, providing a user-friendly web interface for convenient queries. The resulting system is publically available at http://cnv.gtxlab.com /.
Entities:
Keywords:
Copy number variant (CNV); Disease; Named entities recognition; Parallel computing; Relation extraction
Authors: Sandra Brasil; Carlota Pascoal; Rita Francisco; Vanessa Dos Reis Ferreira; Paula A Videira; And Gonçalo Valadão Journal: Genes (Basel) Date: 2019-11-27 Impact factor: 4.096
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