title: Predicting cancer interaction networks using text-mining and structure understanding.
abstract: Extended biomolecule binding or interaction networks can be built by computationally predicting protein-protein interactions from diverse data sources. To construct networks focused on cancer research our approach combines domain specific natural language processing (NLP) assisted text-mining of biomedical literature databases with structure-based protein-protein interaction prediction reinforced with sub-cellular localization and evolutionary information. Fast retrieval of structure-based queries will be accomplished by using a novel knowledge discovery process developed previously.
authors.affiliation: Department of Computer Science, University of Missouri-Columbia, MO 65211, USA.
authors.forename: Christopher M
authors.initials: CM
authors.lastname: Topinka
authors.forename: Chi-Ren
authors.initials: CR
authors.lastname: Shyu
mesh_terms: D019992:Databases as Topic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D009323:Natural Language Processing; D009369:Neoplasms / Q000378:metabolism*; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins