Annals of Biomedical Engineering

, Volume 31, Issue 4, pp 462–470

Recognition of Adenosine Triphosphate Binding Sites Using Parallel Cascade System Identification

  • James R. Green
  • Michael J. Korenberg
  • Robert David
  • Ian W. Hunter
Article

DOI: 10.1114/1.1561293

Cite this article as:
Green, J.R., Korenberg, M.J., David, R. et al. Annals of Biomedical Engineering (2003) 31: 462. doi:10.1114/1.1561293

Abstract

Parallel cascade identification (PCI) is a method for approximating the behavior of a nonlinear system, from input/output training data, by constructing a parallel array of cascaded dynamic linear and static nonlinear elements. PCI has previously been shown to provide an effective means for classifying protein sequences into structure/function families. In the present study, PCI is used to distinguish proteins that are binding to adenosine triphosphate or guanine triphosphate molecules from those that are nonbinding. Classification accuracy of 87.1% using the hydrophobicity scale of Rose et al. (Hydrophobicity of amino acid residues in globular proteins. Science 229:834–838, 1985), and 88.8% using Korenberg's SARAH1 scale, are obtained, as measured by tenfold cross-validation testing. Nearest-neighbor and K-nearest-neighbor (KNN) classifiers are constructed, and the resulting accuracy is, respectively, 88.0% and 90.8% on the SARAH1–encoded test data set, as measured by the above testing protocol. Significantly improved classification accuracy is achieved by combining PCI and KNN classifiers using quadratic discriminant analysis: accuracy rises from 87.9% (PCI) and 87.4% (KNN) to 96.5% for the combination, as measured by twofold cross-validation testing on the SARAH1–encoded test data set. © 2003 Biomedical Engineering Society.

PAC2003: 8714Ee, 8715Cc, 8715Aa

Nonlinear system identification Parallel cascade identification ATP-binding sites SARAH codes Protein sequence analysis 

Copyright information

© Biomedical Engineering Society 2003

Authors and Affiliations

  • James R. Green
    • 1
  • Michael J. Korenberg
    • 1
  • Robert David
    • 2
  • Ian W. Hunter
    • 2
  1. 1.Department of Electrical and Computer EngineeringQueen's UniversityKingstonCanada
  2. 2.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridge

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