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Data mining powered by the gene ontology
Author(s) -
Manda Prashanti
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1359
Subject(s) - association rule learning , cluster analysis , variety (cybernetics) , data mining , computer science , ontology , gene ontology , biological data , data science , data stream mining , concept mining , biomedical text mining , resource (disambiguation) , text mining , information retrieval , bioinformatics , gene , machine learning , artificial intelligence , web mining , biology , world wide web , gene expression , philosophy , biochemistry , epistemology , web service , computer network
Abstract The gene ontology (GO) is a widely used resource for describing molecular functions, biological processes, and cellular components of gene products. Since its inception in 2006, the GO has been used to describe millions of gene products resulting in a massive data store of over 6 million annotations. The staggering amount of data that has resulted from annotating gene products with GO terms has led the way and opened new avenues for a wide variety of large‐scale computational analyses. Specifically, a variety of data mining techniques such as association rule mining, clustering etc. have been applied successfully to a range of biological applications. This article provides a review of four data mining applications/techniques for GO data mining gene expression data, association rule mining, clustering, and text mining and highlights future directions in each of these areas. This article is categorized under: Algorithmic Development > Association Rules Algorithmic Development > Biological Data Mining Ensemble Methods > Text Mining