利用影响性特征选择技术的集合学习从基因表达对癌症进行分类

Nusrath Tabassum, M.A.S. Kamal, M. A. H. Akhand, Kou Yamada
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引用次数: 0

摘要

不受控制的异常细胞生长(即癌症)可能会导致肿瘤、免疫系统衰退和其他致命的残疾。早期癌症识别可使癌症治疗变得更容易,并提高康复率,从而降低死亡率。基因表达数据在早期癌症分类中起着至关重要的作用。由于基因表达数据的高维性相对于较小的样本量,准确的癌症分类是一项复杂而具有挑战性的任务。本研究建议使用降维技术来解决这一限制。具体来说,首先利用互信息(MI)技术选择有影响力的生物标记基因。然后,只使用最有影响力的特征(基因)将集合学习模型应用于缩减后的数据集,从而开发出有效的癌症分类模型。我们选择了以多层感知器(MLP)为基础分类器的装袋法作为集合技术。所提出的癌症分类模型--MI-Bagging 方法--被应用于包含不同癌症类别的多个基准基因表达数据集。将拟议模型的癌症分类准确率与现有的相关方法进行了比较。实验结果表明,尽管高维基因表达数据的规模有限,但所提出的模型优于现有方法,而且能有效地胜任癌症分类。所提方法达到的最高准确率表明,所提出的基于基因表达的新兴癌症分类器有望帮助癌症治疗,并在未来提高癌症生存率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cancer Classification from Gene Expression Using Ensemble Learning with an Influential Feature Selection Technique
Uncontrolled abnormal cell growth, known as cancer, may lead to tumors, immune system deterioration, and other fatal disability. Early cancer identification makes cancer treatment easier and increases the recovery rate, resulting in less mortality. Gene expression data play a crucial role in cancer classification at an early stage. Accurate cancer classification is a complex and challenging task due to the high-dimensional nature of the gene expression data relative to the small sample size. This research proposes using a dimensionality-reduction technique to address this limitation. Specifically, the mutual information (MI) technique is first utilized to select influential biomarker genes. Next, an ensemble learning model is applied to the reduced dataset using only the most influential features (genes) to develop an effective cancer classification model. The bagging method, where the base classifiers are Multilayer Perceptrons (MLPs), is chosen as an ensemble technique. The proposed cancer classification model, the MI-Bagging method, is applied to several benchmark gene expression datasets containing distinctive cancer classes. The cancer classification accuracy of the proposed model is compared with the relevant existing methods. The experimental results indicate that the proposed model outperforms the existing methods, and it is effective and competent for cancer classification despite the limited size of gene expression data with high dimensionality. The highest accuracy achieved by the proposed method demonstrates that the proposed emerging gene-expression-based cancer classifier has the potential to help in cancer treatment and lead to a higher cancer survival rate in the future.
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