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MCR-Miner: Maximal Confident Association Rules Miner Algorithm for Up/Down-Expressed Genes |
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PP: 799-809 |
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Author(s) |
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Wael Zakaria,
Yasser Kotb,
Fayed Ghaleb,
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Abstract |
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| DNA microarrays allow simultaneous measurements of expression levels for a large number of genes across a number
of different experimental conditions (samples). The algorithms for mining association rules are used to reveal biologically relevant
associations between different genes under different experimental samples. This paper presents a new column-enumeration based
method algorithm (abbreviated by MCR-Miner) for mining maximal high confidence association rules for up/down-expressed genes.
MCR-Miner algorithm uses an efficient maximal association rules tree data structure (abbreviated by MAR-Tree). MAR-tree
enumerates (lists) all genes with their binary representations, the binary representation of a gene saves the status (normal, up, and downexpressed)
of a gene in all experiments. The binary representation has many advantages, scan the dataset only once, the measurements
of confidences for association rules are made in one step, and it makes MCR-Miner algorithm easily finds all maximal high confidence
association rules. In the experimental results on a real microarray datasets, MCR-Miner algorithm attained very promising results and
outperformed other counterparts. |
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