The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
miRCancer: a microRNA–cancer association database constructed by text mining on literature
Abstract
Motivation: Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA–cancer associations predicted by computational methods but few from empirical results. Despite the fact that abundant experiments investigating microRNA expressions in cancer cells have been carried out, the results have remain scattered in the literature. We propose to extract microRNA–cancer associations by text mining and store them in a database called miRCancer. Results: The text mining is based on 75 rules we have constructed, which represent the common sentence structures typically used to state microRNA expressions in cancers. The microRNA–cancer association database, miRCancer, is updated regularly by running the text mining algorithm against PubMed. All miRNA–cancer associations are confirmed manually after automatic extraction. miRCancer currently documents 878 relationships between 236 microRNAs and 79 human cancers through the processing of >26 000 published articles. Availability: miRCancer is freely available on the web at http://mircancer.ecu.edu/ Contact: dingq@ecu.edu
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Cites
The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14
Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993) · Cell · doi:10.1016/0092-8674(93)90529-y · Cited by 12,948
MicroRNA Gene Expression Deregulation in Human Breast Cancer
Iorio, M. V., Ferracin, M., Liu, C.-G., Veronese, A., Spizzo, R., Sabbioni, S., Magri, E., Pedriali, M., Fabbri, M., Campiglio, M., et al. (2005) · Cancer Research · doi:10.1158/0008-5472.can-05-1783 · Cited by 4,053
Cited by
Liu, Y., Zeng, X., He, Z., & Zou, Q. (2017) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2550432 · Cited by 284
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Zeng, X., Liu, L., Lü, L., & Zou, Q. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty112 · Cited by 235
Comparative assessment of differential network analysis methods
Lichtblau, Y., Zimmermann, K., Haldemann, B., Lenze, D., Hummel, M., & Leser, U. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw061 · Cited by 76
Biomedical data, computational methods and tools for evaluating disease–disease associations
Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49
An ontology-based knowledge graph for representing interactions involving RNA molecules
Cavalleri, E., Cabri, A., Soto-Gomez, M., Bonfitto, S., Perlasca, P., Gliozzo, J., Callahan, T. J., Reese, J., Robinson, P. N., Casiraghi, E., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03673-7 · Cited by 33
Metadata
- Authors
- Xie, B., Ding, Q., Han, H., & Wu, D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-01-16
- Volume
- 29
- Issue
- 5
- Pages
- 638-644
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2103151642
- Cited by
- 572
- References
- 2
- FWCI
- 10.76
- Type
- journal article
- Language
- en
- Open access
- closed
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