Artif Intell Med - Multi-way association extraction and visualization from biological text documents using hyper-graphs: applications to genetic association studies for diseases.


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JECTIVES: Biological research literature, as in many other domains of human endeavor, represents a rich, ever growing source of knowledge. An important form of such biological knowledge constitutes associations among biological entities such as genes, proteins, diseases, drugs and chemicals, etc. There has been a considerable amount of recent research in extraction of various kinds of binary associations (e.g., gene-gene, gene-protein, protein-protein, etc.) using different text mining approaches. However, an important aspect of such associations (e.g., "gene A activates protein B") is identifying the context in which such associations occur (e.g., "gene A activates protein B in the context of disease C in organ D under the influence of chemical E"). Such contexts can be represented appropriately by a multi-way relationship involving more than two objects (e.g., objects A, B, C, D, E) rather than usual binary relationship (objects A and B).METHODS: Such multi-way relations naturally lead to a hyper-graph representation of the knowledge rather than a binary graph. The hyper-graph based multi-way knowledge extraction from biological text literature represents a computationally difficult problem (due to its combinatorial nature) which has not received much attention from the Bioinformatics research community. In this paper, we describe and compare two different approaches to such multi-way hyper-graph extraction: one based on an exhaustive enumeration of all multi-way hyper-edges and the other based on an extension of the well-known A Priori algorithm for structured data to the case unstructured textual data. We also present a representative graph based approach towards visualizing these genetic association hyper-graphs.RESULTS: Two case studies are conducted for two biomedical problems (related to the diseases of lung cancer and colorectal cancer respectively), illustrating that the latter approach (using the text-based A Priori method) identifies the same hyper-edges as the former approach (the exhaustive method), but at a much less computational cost. The extracted hyper-relations are presented in the paper as cognition-rich representative graphs, representing the corresponding hyper-graphs.CONCLUSIONS: The text-based A Priori algorithm is a practical, useful method to extract hyper-graphs representing multi-way associations among biological objects. These hyper-graphs and their visualization using representative graphs can provide important contextual information for understanding gene-gene associations relevant to specific diseases.

Resumo Limpo

jectiv biolog research literatur mani domain human endeavor repres rich ever grow sourc knowledg import form biolog knowledg constitut associ among biolog entiti gene protein diseas drug chemic etc consider amount recent research extract various kind binari associ eg genegen geneprotein proteinprotein etc use differ text mine approach howev import aspect associ eg gene activ protein b identifi context associ occur eg gene activ protein b context diseas c organ d influenc chemic e context can repres appropri multiway relationship involv two object eg object b c d e rather usual binari relationship object bmethod multiway relat natur lead hypergraph represent knowledg rather binari graph hypergraph base multiway knowledg extract biolog text literatur repres comput difficult problem due combinatori natur receiv much attent bioinformat research communiti paper describ compar two differ approach multiway hypergraph extract one base exhaust enumer multiway hyperedg base extens wellknown priori algorithm structur data case unstructur textual data also present repres graph base approach toward visual genet associ hypergraphsresult two case studi conduct two biomed problem relat diseas lung cancer colorect cancer respect illustr latter approach use textbas priori method identifi hyperedg former approach exhaust method much less comput cost extract hyperrel present paper cognitionrich repres graph repres correspond hypergraphsconclus textbas priori algorithm practic use method extract hypergraph repres multiway associ among biolog object hypergraph visual use repres graph can provid import contextu inform understand genegen associ relev specif diseas

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