Design of a smart biomarker for bioremediation: A machine learning approach
Design of a smart biomarker for bioremediation: A machine learning approach
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DOI：
10.1016/j.compbiomed.2011.03.013
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发表时间：
2011-06-01
影响因子：
7.7
通讯作者：
Iyengar, Puneeth
中科院分区：
文献类型：
--
作者：
Kumar, P. T. Krishna;Vinod, P. T.;Iyengar, Puneeth
许多微量元素(TE)天然存在于海洋环境中，对人体健康具有决定性作用。紫贻贝是TE的丰富来源，但由于生长在工业排污口附近，它们会因TE浓度升高而受到污染，并作为污染的生物标志物。随着生物修复对机器学习数据处理技术的依赖程度越来越高，我们提出了利用MG进行生物修复的信息论概念。MG的原位生物修复是通过行列式不等式技术降低TE的浓度和最大化互信息(MI)来实现的，而不需要在外部添加任何化学元素。我们提出了我们的MI技术在预测MG中较低浓度的Cd和Pd的生物修复中的优势。(C)2011爱思唯尔有限公司。保留所有权利。
Many trace elements (TE) occur naturally in marine environments and accomplish decisive functions in humans to maintain good health. Mytilus galloprovincialis (MG) is a rich source of TE, but since it is grown near industrial outfalls, they become polluted with elevated levels of TE concentration and serve as biomarkers of pollution. As bioremediation is increasingly reliant on machine learning data processing techniques, we propose the information theoretic concept of using MG for bioremediation. The in situ bioremediation in MG is accomplished by reduction in concentration of TE by the technique of determinant inequalities and the maximization of Mutual Information (MI) without adding any chemical element externally. We bring out the superiority of our technique of MI over that of Principal Component Analysis (PCA) in predicting lower concentration for bioremediation of Cd and Pb in MG. (C) 2011 Elsevier Ltd. All rights reserved.