Multi-way association extraction and visualization from biological text documents using hyper-graphs: Applications to genetic association studies for diseases

摘要

ObjectivesBiological 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).