Exploring ant-based algorithms for gene expression data analysis
Exploring ant-based algorithms for gene expression data analysis
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DOI：
10.1016/j.artmed.2009.03.004
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发表时间：
2009-10-01
影响因子：
7.5
通讯作者：
Hui, Siu Cheung
中科院分区：
文献类型：
--
作者：
He, Yulan;Hui, Siu Cheung
目的：最近，已经提出了许多研究使用自然启发的算法来执行复杂的机器学习任务。蚁群优化（ACO）是一种基于群体智能的算法，它是从蚂蚁的集体觅食行为启发的模型中衍生出来的。本文利用蚁群算法的自组织性和鲁棒性等特点，研究了基于蚁群算法的基因表达数据聚类和关联分类算法。方法和内容：针对基因表达数据分析，提出了基于蚁群算法的聚类（Ant-C）和基于蚁群算法的关联规则挖掘（Ant-ARM）。所提出的算法利用蚂蚁的自然行为，如合作和适应，以允许一个灵活的强大的搜索一个好的候选solution.Results：Ant-C已被测试的三个数据集上选择的斯坦福大学基因组资源数据库，并取得了相对较高的精度相比，其他经典的聚类方法。Ant-ARM已经在急性淋巴细胞白血病（ALL）/急性髓细胞白血病（AML）数据集上进行了测试，并产生了大约30个分类规则，具有很高的准确率。结论：Ant-C可以产生最佳数量的聚类，而不需要结合任何其他算法，如K-means或凝聚层次聚类。对于关联分类，虽然一些著名的算法，如Apriori，FP-growth和Magnum Opus无法在合理的时间内从ALL/AML数据集中挖掘任何关联规则，但Ant-ARM能够提取关联分类规则。(C)2009 Elsevier B. V.保留所有权利。
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. (C) 2009 Elsevier B.V. All rights reserved.