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Supervised Learning-Aided Optimization of Expert-Driven Functional Protein Sequence Annotation

  • Lev Soinov
  • Alexander Kanapin
  • Misha Kapushesky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)

Abstract

The aim of this work is to use a supervised learning approach to identify sets of motif-based sequence characteristics, combinations of which can give the most accurate annotation of new proteins. We assess several of InterPro Consortium member databases for their informativeness for the annotation of full-length protein sequences. Thus, our study addresses the problem of integrating biological information from various resources. Decision-rule algorithms are used to cross-map different biological classification systems in order to optimise the process of functional annotation of protein sequences. Various features (e.g., keywords, GO terms, structural complex names) may be assigned to a sequence via its characteristics (e.g., motifs built by various protein sequence analysis methods) with the developed approach. We chose SwissProt keywords as the set of features on which to perform our analysis. From the presented results one can quickly obtain the best combinations of methods appropriate for the description of a given class of proteins.

Keywords

Decision Rule Feature Subset Selection Misclassification Cost Relative Informativeness Supervise Learning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Lev Soinov
    • 1
  • Alexander Kanapin
    • 2
  • Misha Kapushesky
    • 3
  1. 1.Algorithms and methodsEBI, Wellcome Trust Genome CampusHinxton, CambridgeUK
  2. 2.InterPro and SwissProt data retrieval and encodingEBI, Wellcome Trust Genome CampusHinxton, CambridgeUK
  3. 3.Calculations and programmingEBI, Wellcome Trust Genome CampusHinxton, CambridgeUK

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