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Discovery of genes that are responsible for various diseases, becomes an important task. Since the genes are related with many diseases, the gene-disease association should be discovered. To obtain this gene-disease association from available biomedical literature, the relation type between the gene and disease is extracted from the biomedical literature. So, this becomes more and more important to deal with the extraction problem from the biomedical texts in an automatic way. Then the gene-disease association is visualized by network construction and association score matrix is constructed to calculate the gene-disease association score. The gene-disease relation type is identified and then the association score is calculated by integrating disease similarity network and protein-protein interaction network. The candidate genes for the particular disease and the novel genes for various diseases can also be found by calculating the association score and visualizing the dataset network.
- Allen PD., Wiegers TC., Johnson RJ., Lay JM., Lennon-Hopkins K., Saraceni-Richards C., Sciaky D., Murphy CG and Mattingly CJ. (2013), ‘Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database’, PLoS One., pp 8, e58201.
- Hamosh A., Scott AF. Amberger JS. Bocchini CA and McKusick VA. (2005), ‘Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders’, Nucleic acids research, pp 33, D514-517.
- Kalpana Raja, Suresh Subramani and Jeyakumar Natarajan (2014) ‘A hybrid named entity recognition for tagging human proteins/genes’, International journal of data mining and bioinformatics 10.3, pp 315-328.
- Markus Bundschus ,MathaeusDejori, Martin Stetter,VolkerTresp and Hans Peter Kriegel (2008), ‘Extraction of semantic biomedical relations from text usingconditional random fields’, BMC Bioinformatics.,pp 9, 207.
- Panagiota I. Kontou , AthanasiaPavlopoulou , NikiL.Dimou , Georgios A. Pavlopoulos and PantelisG.Bagos (2016), ‘Network analysis of genes and their association with diseases’, Gene 590, pp 68–78.
- Peng Gang Sun (2015), ‘The human Drug–Disease-Gene Network’, Information Sciences 306, pp 70–80
- Suresh Subramani, Raja Kalpana and Jeyakumar Natarajan (2014), ‘ProNormz – An integrated approach for human proteins and protein kinases normalization’, Journal of Biomedical Informatics 47, pp 131–138.
- Suresh Subramani and Jeyakumar Natarajan (2015) ‘An integrated text mining system based on network analysis for knowledge discovery of human gene-disease associations (GenDisFinder)’, Proceedings of the fifth BioCreative challenge workshop 2015.
- Vanunu O., Magger O., Ruppin E., Sholomi T., and Sharan R. (2010) ,‘Associating genes and Protein Complexes with Disease via Network Propagation’, PloSComputBiol 6(1): e1000641.
- XingliGuo,LinGao ,ChunshuiWei,XiaofeiYang,YiZhao,Anguo Dong (2011) ‘A computational method based on the integration of heterogeneous networks for predicting disease-gene associations’. PLoS One., 6, e24171.
[Kanimozhi U and Manjula D. (2017); ASSOCIATION MINING FROM BIOMEDICAL TEXT WITH NETWORK ANALYSIS Int. J. of Adv. Res. 5 (1). 158-169] (ISSN 2320-5407). www.journalijar.com
Article DOI: 10.21474/IJAR01/2733 DOI URL: http://dx.doi.org/10.21474/IJAR01/2733
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