Discovering Patterns in Gene Ontology Using Association Rule Mining

Michael Hahsler and Anurag Nagar
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Michael Hahsler: Department of Engineering Management, Information, and Systems, Southern Methodist University, USA
Anurag Nagar: Department of Computer Science, University of Texas at Dallas, USA

Biostatistics and Biometrics Open Access Journal, 2018, vol. 6, issue 3, 99-101

Abstract: Gene Ontology (GO) is one of the largest interdisciplinary bioinformatics projects that aims to provide a uniform and consistent representation of genes and gene products across different species. It has fast become a vast repository of data consisting of biological terms arranged in the form of three different ontologies, and annotation files that represent how these terms are linked to genes across different organisms. Further, this dataset is ever growing due to the various genomic projects underway. While this growth in data is a very welcome development, there is a critical need to develop data mining tools and algorithms that can extract summaries, and discover useful knowledge in an automated way. This paper presents a review of the efforts in this area, focusing on information discovery in the form of association rule mining.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:6:y:2018:i:3:p:99-101

DOI: 10.19080/BBOAJ.2018.06.555689

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