Exploring ant-based algorithms for gene expression data analysis

Yulan He, Siu C. Hui

Research output: Contribution to journalArticlepeer-review


Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.
Original languageEnglish
Pages (from-to)105-119
Number of pages15
JournalArtificial Intelligence in Medicine
Issue number2
Publication statusPublished - Oct 2009

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Artificial intelligence in medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in He, Y & Hui, SC, 'Exploring ant-based algorithms for gene expression data analysis' Artificial intelligence in medicine, vol 47, no. 2 (2009) DOI http://dx.doi.org/10.1016/j.artmed.2009.03.004.


  • gene expression data analysis
  • ant colony optimization
  • clustering
  • associative classification
  • swarm intelligence


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