Design of a smart biomarker for bioremediation: A machine learning approach

  • Authors:

  • P. T. Krishna Kumar;P. T. Vinod;Vir V. Phoha;S. S. Iyengar;Puneeth Iyengar

  • Affiliations:

  • Room No.: 114, Central Design Office, Indira Gandhi Centre for Atomic Research, Kalpakkam-603102, Tamilnadu, India;School of Information Technology Engineering, VIT University, Vellore-632014, Tamilnadu, India;College of Engineering and Science, Louisiana Tech University, Nethken Hall, Arizona Ave, Ruston, LA 71272, USA;Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA;UT Southwestern Medical Center, Dallas, TX, USA

  • Venue:
  • Computers in Biology and Medicine
  • Year:
  • 2011

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Abstract

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.