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Neural Computing and Applications

Erschienen in:

01.09.2013 | Original Article

Two novel interestingness measures for gene association rule mining

verfasst von: Meihua Wang, Shumin Wu, Ruichu Cai

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

Recent research has shown that association rules are useful in gene expression data analysis. Interestingness measure plays an important role in the association rule mining on small sample size, high dimensionality, and noisy gene expression data. This work introduces two interestingness measures by exploring prior knowledge contained in open biological databases. They are Max-Pathway-Distance (MaxPD), which explores the gene’s relativity in Kyoto encyclopedia of genes and genomes Pathway, and Max-Chromosomal-Distance (MaxCD), which makes use of the distance among genes in the chromosome. The properties of our proposed interestingness measures are also explored to mine the interesting rules efficiently. Experimental results on four real-life gene expression datasets show the effectiveness of MaxPD and MaxCD in both classification accuracy and biological interpretability.

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Titel: Two novel interestingness measures for gene association rule mining
verfasst von: Meihua Wang
Shumin Wu
Ruichu Cai
Publikationsdatum: 01.09.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1005-3

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