Genomic and proteomic sequence recognition using a connectionist inference model

Authors

  • Siri Bavan,

    1. School of Computer Science and Mathematics, University of Portsmouth, Mercantile House, Hampshire Terrace, Portsmouth PO1 2EG, UK
    Search for more papers by this author
  • Martyn Ford,

    Corresponding author
    1. Institute for Biomedical and Biomolecular Sciences, Centre for Molecular Design, University of Portsmouth, King Henry Building, King Henry I Street, Portsmouth PO1 2DY, UK
    • Martyn Ford Institute for Biomedical and Biomolecular Sciences, Centre for Molecular Design, University of Portsmouth, King Henry Building, King Henry I Street, Portsmouth PO1 2DY, UK
    Search for more papers by this author
  • Melina Kalatzi

    1. Institute for Biomedical and Biomolecular Sciences, Centre for Molecular Design, University of Portsmouth, King Henry Building, King Henry I Street, Portsmouth PO1 2DY, UK
    Search for more papers by this author

Abstract

In this paper a proposal for implementing a connectionist associative memory model (CAMM) based on a novel approach for recognising sequences is presented. The objective of the CAMM is to satisfy medium-high capacity and the retrieval of an arbitrary number of multiple associative memories that satisfy the stimulus input. The architecture is constructed on-the-fly and is dependent on the information in the training set. The model is composed of two stages; StageI and StageII. StageI is concerned with the development of a state space graph representing the training set and embedding that graph in a connectionist model. During retrieval a graph is produced that represents the candidate solutions; some spurious memories may infiltrate the solution space which is removed in StageII using conventional techniques.

© 2000 Society of Chemical Industry

Ancillary