Yaqoob Abrar, Mir Mushtaq Ahmad, Jagannadha Rao G V V, Tejani Ghanshyam G
School of Advanced Science and Language, VIT Bhopal University, Kothrikalan, Sehore, Bhopal 466114, India.
Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia.
Diagnostics (Basel). 2024 Nov 22;14(23):2632. doi: 10.3390/diagnostics14232632.
Accurate classification in cancer research is vital for devising effective treatment strategies. Precise cancer classification depends significantly on selecting the most informative genes from high-dimensional datasets, a task made complex by the extensive data involved. This study introduces the Two-stage MI-PSA Gene Selection algorithm, a novel approach designed to enhance cancer classification accuracy through robust gene selection methods. The proposed method integrates Mutual Information (MI) and Particle Swarm Optimization (PSO) for gene selection. In the first stage, MI acts as an initial filter, identifying genes rich in cancer-related information. In the second stage, PSO refines this selection to pinpoint an optimal subset of genes for accurate classification. The experimental findings reveal that the MI-PSA method achieves a best classification accuracy of 99.01% with a selected subset of 19 genes, substantially outperforming the MI and SVM methods, which attain best accuracies of 93.44% and 91.26%, respectively, for the same gene count. Furthermore, MI-PSA demonstrates superior performance in terms of average and worst-case accuracy, underscoring its robustness and reliability. The MI-PSA algorithm presents a powerful approach for identifying critical genes essential for precise cancer classification, advancing both our understanding and management of this complex disease.
在癌症研究中，准确分类对于制定有效的治疗策略至关重要。精确的癌症分类很大程度上取决于从高维数据集中选择最具信息性的基因，而大量的数据使得这项任务变得复杂。本研究介绍了两阶段互信息 - 粒子群优化基因选择算法（Two-stage MI-PSA Gene Selection algorithm），这是一种通过强大的基因选择方法来提高癌症分类准确性的新方法。所提出的方法将互信息（MI）和粒子群优化（PSO）集成用于基因选择。在第一阶段，互信息作为初始过滤器，识别富含癌症相关信息的基因。在第二阶段，粒子群优化对该选择进行细化，以确定用于准确分类的最佳基因子集。实验结果表明，MI-PSA方法在选择19个基因的子集时实现了99.01%的最佳分类准确率，大大优于MI和支持向量机（SVM）方法，对于相同数量的基因，MI和SVM方法的最佳准确率分别为93.44%和91.26%。此外，MI-PSA在平均准确率和最坏情况准确率方面表现出卓越的性能，突出了其稳健性和可靠性。MI-PSA算法为识别精确癌症分类所需的关键基因提供了一种强大的方法，推动了我们对这种复杂疾病的理解和管理。
