Abstract
Association rules mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rules mining model have been proposed so far, however, the problem of mining for mutually exclusive items has not been investigated. Such information could be useful in various cases in many application domains like bioinformatics (e.g. when the expression of a gene excludes the expression of another) In this paper, we address the problem of mining pairs and triples of genes, such that the presence of one excludes the presence of the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we apply on gene expression data gaining new biological insights.
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Tzanis, G., Vlahavas, I. (2010). Mining for Mutually Exclusive Gene Expressions. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_29
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DOI: https://doi.org/10.1007/978-3-642-12842-4_29
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