Linking Biomedical Data for Disease-SNP Relation Discovery

摘要: Traditional relation discovery is always conducted through either text mining or database analysis. However, in the real world, knowledge exists different formats and can be expressed a variety of forms. Discovering relations between diseases single-nucleotide polymorphisms (SNPs) challenging because difficulties unstructured data processing distributed heterogeneous integration. With development Sematic Web theory technology, it provides feasibility to reconstruct traditional integration process sematic manner biomedical big era. Our study aims discover disease-SNP integrated linked facilitate scientific research analyses reduce biological experiment costs. We focus on investigating capability techniques integrating relationships diseases, genes, SNPs. To demonstrate effectiveness our proposed method, we case Alzheimer’s disease-SNPs by 10 datasets.