Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, India.
Department of Information Technology, Anna University Regional Campus, Coimbatore, India.
J Med Syst. 2018 Feb 21;42(4):61. doi: 10.1007/s10916-018-0910-0.
In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to analyze these samples. The fuzzy expert system offers a clear classification when compared to the machine learning and statistical methodologies. In fuzzy classification, knowledge acquisition would be a major concern. Despite several existing approaches for knowledge acquisition much effort is necessary to enhance the learning process. This paper proposes an innovative Hybrid Stem Cell (HSC) algorithm that utilizes Ant Colony optimization and Stem Cell algorithm for designing fuzzy classification system to extract the informative rules to form the membership functions from the microarray dataset. The HSC algorithm uses a novel Adaptive Stem Cell Optimization (ASCO) to improve the points of membership function and Ant Colony Optimization to produce the near optimum rule set. In order to extract the most informative genes from the large microarray dataset a method called Mutual Information is used. The performance results of the proposed technique evaluated using the five microarray datasets are simulated. These results prove that the proposed Hybrid Stem Cell (HSC) algorithm produces a precise fuzzy system than the existing methodologies.
在不断发展的情况下，由于微阵列数据提供了对疾病中遗传变异的更全面的了解，因此广泛用于医学研究。由于基因表达样本具有高维度，因此手动分析样本变得很繁琐。因此，需要一个自动化系统来分析这些样本。与机器学习和统计方法相比，模糊专家系统提供了更清晰的分类。在模糊分类中，知识获取将是一个主要关注点。尽管存在几种现有的知识获取方法，但仍需要付出很大的努力来增强学习过程。本文提出了一种创新的混合干细胞（HSC）算法，该算法利用蚁群优化和干细胞算法来设计模糊分类系统，从微阵列数据集中提取信息规则以形成隶属函数。HSC 算法使用新颖的自适应干细胞优化（ASCO）来改进隶属函数的点，并使用蚁群优化来生成接近最优的规则集。为了从大型微阵列数据集中提取最有信息的基因，使用了一种称为互信息的方法。使用五个微阵列数据集评估了所提出技术的性能结果进行了模拟。这些结果证明，与现有方法相比，所提出的混合干细胞（HSC）算法产生了更精确的模糊系统。
