Protein function prediction via graph kernels

Karsten Borgwardt, Cheng Soon Ong, Stefan Schoenauer, S Vishwanathan, Alexander Smola, Hans-Peter Kriegel

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.
    Original languageEnglish
    Pages (from-to)147-156
    JournalBioinformatics
    Volume21
    Issue numberSupplement 1
    DOIs
    Publication statusPublished - 2005

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