Alshamlan Hala Mohammed
Information Technology Department, King Saud University, Riyadh, Saudi Arabia.
Saudi J Biol Sci. 2018 Jul;25(5):895-903. doi: 10.1016/j.sjbs.2017.12.012. Epub 2018 Jan 3.
In this paper, we propose a new hybrid method based on Correlation-based feature selection method and Artificial Bee Colony algorithm,namely Co-ABC to select a small number of relevant genes for accurate classification of gene expression profile. The Co-ABC consists of three stages which are fully cooperated: The first stage aims to filter noisy and redundant genes in high dimensionality domains by applying Correlation-based feature Selection (CFS) filter method. In the second stage, Artificial Bee Colony (ABC) algorithm is used to select the informative and meaningful genes. In the third stage, we adopt a Support Vector Machine (SVM) algorithm as classifier using the preselected genes form second stage. The overall performance of our proposed Co-ABC algorithm was evaluated using six gene expression profile for binary and multi-class cancer datasets. In addition, in order to proof the efficiency of our proposed Co-ABC algorithm, we compare it with previously known related methods. Two of these methods was re-implemented for the sake of a fair comparison using the same parameters. These two methods are: Co-GA, which is CFS combined with a genetic algorithm GA. The second one named Co-PSO, which is CFS combined with a particle swarm optimization algorithm PSO. The experimental results shows that the proposed Co-ABC algorithm acquire the accurate classification performance using small number of predictive genes. This proofs that Co-ABC is a efficient approach for biomarker gene discovery using cancer gene expression profile.
在本文中，我们提出了一种基于基于相关性的特征选择方法和人工蜂群算法的新型混合方法，即协同人工蜂群算法（Co-ABC），用于选择少量相关基因以对基因表达谱进行准确分类。Co-ABC由三个完全协作的阶段组成：第一阶段旨在通过应用基于相关性的特征选择（CFS）过滤方法来过滤高维域中的噪声和冗余基因。在第二阶段，使用人工蜂群（ABC）算法来选择信息丰富且有意义的基因。在第三阶段，我们采用支持向量机（SVM）算法作为分类器，使用第二阶段预选的基因。我们提出的Co-ABC算法的整体性能使用六个用于二元和多类癌症数据集的基因表达谱进行了评估。此外，为了证明我们提出的Co-ABC算法的效率，我们将其与先前已知的相关方法进行了比较。为了进行公平比较，使用相同的参数重新实现了其中两种方法。这两种方法是：Co-GA，即CFS与遗传算法GA相结合；第二种方法名为Co-PSO，即CFS与粒子群优化算法PSO相结合。实验结果表明，所提出的Co-ABC算法使用少量预测基因即可获得准确的分类性能。这证明了Co-ABC是一种使用癌症基因表达谱发现生物标志物基因的有效方法。
