Protein Classification from Protein-Domain and Gene-Ontology Annotation Information Using Formal Concept Analysis

  • Authors:

  • Mi-Ryung Han;Hee-Joon Chung;Jihun Kim;Dong-Young Noh;Ju Han Kim

  • Affiliations:

  • Seoul National University Biomedical Informatics (SNUBI),;Seoul National University Biomedical Informatics (SNUBI),;Seoul National University Biomedical Informatics (SNUBI),;Department of Surgery, and Cancer Research Institute,;Seoul National University Biomedical Informatics (SNUBI), and Human Genome Research Institute, Seoul National University College of Medicine, Seoul, Korea

  • Venue:
  • ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
  • Year:
  • 2007

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Abstract

There are a number of different attributes to describe ontology of proteins such as protein structure, biomolecular interaction, cellular location, and protein domains which represent the basic evolutionary units that form protein. In this paper, we propose a mathematical approach, formal concept analysis (FCA), which toward abstracting from attribute-based object descriptions. Based on this theory, we present extended version of algorithm, tripartite lattice, to compute a concept lattice. By analyzing tripartite lattice, we attempt to extract proteins, which are related to domains and gene ontology (GO) terms from bottom nodes to the top of lattice. In summary, using tripartite lattices, we classified proteins from protein domain composition with their describing gene ontology (GO) terms.