期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 41, 期 6, 页码 357-360出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2011.03.013
关键词
Mytilus galloprovincialis; Biomarker; Reduction of trace element concentration for bioremediation; Covariance matrix; Information theory; Technique of determinant inequalities; Maximization of Mutual Information for bioremediation; Principal Component Analysis
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.
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