Abstract
Gene Ontology is a structured repository of concepts that are associated to one or more gene products. The Gene Ontology describes gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. There are different approaches available to discover the biologically relevant associations between terms of Gene Ontology. Multiple genomic and proteomic semantic annotations scattered in many distributed and heterogeneous data sources such heterogeneity and dispersion hamper the biologists’ ability of asking global queries and performing global evaluations. To overcome this problem, we developed a knowledge based framework to create and maintain a Genomic and Proteomic Knowledge Base, which integrates several of the most relevant sources. We have developed an approach using Multi-Ontology data mining at All Levels (MOAL) to mine cross-ontology association rules, i.e. rules that involve Gene Ontology terms present in its sub-ontologies for identifying the gene attacked disease.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cannataro, M., Guzzi, P.H., Sarica, A.: Data Mining and-Life Sciences Applications on the Grid. Wiley Interdisc, Hoboken (2013). Data Min. Knowl. Discov. 3(3), 216–238
Masseroli, M., Galati, O., Pinciroli, F.: GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists. Nucleic Acids Res. 33, W717–W723 (2005)
Davis, M.J., Sehgal, M.B., Ragan, M.A.: Automatic, context-specific generation of Gene Ontology slims. BMC Bioinform. 11, 498 (2010)
Agapito, G., Cannataro, M., Guzzi, P.H., Milano, M.: Using GO-WAR for Mining Cross-Ontology Weighted Association Rules. Elsevier, New York (2015)
Carmona-Saez, P., Chagoyen, M., Rodriguez, A., Trelles, O., Carazo, J.M.: Integrated analysis of gene expression by Association Rules Discovery. BMC Bioinform. 7, 54 (2006)
van Hemert, J., Baldock, R.: Mining spatial gene expression data for association rules. In: Hochreiter, S., Wagner, R. (eds.) BIRD 2007. LNCS, vol. 4414, pp. 66–76. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71233-6_6
https://www.omim.org/entry/, http://www.orpha.net/consor/cgibin/index.php,www.malacards.org/card/
Manda, P., Ozkan, S., Wang, H., McCarthy, F., Bridges, S.M.: Cross-ontology multi-level association rule mining in the gene ontology. PLoS ONE 7(10), e47411 (2012)
Hahsler, M., Greun, B., Hornik, K.: Arules: mining association rules and frequent itemsets (2007)
Davis, M.J., Sehgal, M.S., Ragan, M.A.: Automatic, context-specific generation of Gene Ontology slims. BMC Bioinform. 11, 498 (2010)
Kate, A.V., Balakrishnan, H.: Ontology mining in Molecular Biology domain (Lac Operon problem) (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Haritha, P., Priyatharshini, R., Abishek, A.G., Kamala Kiran, V. (2018). Knowledge Based Framework for Genetic Disease Diagnosis Using Data Mining Technique. In: Uden, L., Hadzima, B., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2018. Communications in Computer and Information Science, vol 877. Springer, Cham. https://doi.org/10.1007/978-3-319-95204-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-95204-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95203-1
Online ISBN: 978-3-319-95204-8
eBook Packages: Computer ScienceComputer Science (R0)