Arsenic is a carcinogen, and long-term exposure to it may result in the development of multi-organ disease. Understanding the underlying intricate molecular network of toxicity and carcinogenicity is crucial for identifying a small set of differentially expressed biomarker genes to predict the risk of the exposed population. In this paper, a multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique has been proposed to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. To achieve the optimal classification accuracy along with the minimum number of genes, a multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) has been utilized. The proposed MO-RSplbestPSO also guides the multiple kernel learning mechanism which provides data specific classification. The proposed computational framework has been applied to the developed whole genome DNA microarray prepared using blood samples collected from a specific arsenic exposed area of the Indian state of West Bengal. A set of twelve biomarker genes, with four novel genes, are successfully identified for the classification of exposure to arsenic and its subcategories, which can be used as future prognostic biomarkers for screening of arsenic exposed populations. Also, the biological significance of each gene is detailed to delineate the complex molecular networking and mode of toxicity.

中文翻译：

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采用多核学习嵌入式多目标群体智能识别砷中毒的生物标志物


砷是一种致癌物质，长期接触砷可能导致多器官疾病。了解潜在的复杂的毒性和致癌性分子网络对于识别一小组差异表达的生物标志物基因以预测暴露人群的风险至关重要。本文提出了一种多核学习（MKL）嵌入式多目标群体智能技术，用于从砷中毒样本的转录组谱中识别候选生物标志物基因。为了实现最佳分类精度和最少基因数量，采用了多目标随机空间局部最佳粒子群优化（MO-RSplbestPSO）。所提出的 MO-RSplbestPSO 还指导提供数据特定分类的多核学习机制。所提出的计算框架已应用于开发的全基因组 DNA 微阵列，该微阵列是使用从印度西孟加拉邦特定砷暴露地区采集的血液样本制备的。成功鉴定了一组十二个生物标志物基因，其中有四个新基因，用于砷暴露及其亚类的分类，可用作未来砷暴露人群筛查的预后生物标志物。此外，还详细描述了每个基因的生物学意义，以描述复杂的分子网络和毒性模式。