Abstract
Traditional relation discovery is always conducted through either text mining or database analysis. However, in the real world, knowledge exists in different formats and can be expressed in a variety of forms. Discovering relations between diseases and single-nucleotide polymorphisms (SNPs) is challenging because of difficulties in unstructured data processing or distributed heterogeneous data integration. With the development of Sematic Web theory and technology, it provides feasibility to reconstruct the traditional data integration process in a sematic manner in the biomedical big data era. Our study aims to discover disease-SNP relation in integrated linked data to facilitate scientific research analyses and reduce biological experiment costs. We focus on investigating the capability of linked data techniques in integrating and mining relationships between diseases, genes, and SNPs. To demonstrate the effectiveness of our proposed method, we conducted a case study in Alzheimer’s disease-SNPs discovery by integrating 10 datasets.
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Hong, N., Qian, Q., Fang, A., Wu, S., Wang, J. (2013). Linking Biomedical Data for Disease-SNP Relation Discovery. In: Qi, G., Tang, J., Du, J., Pan, J.Z., Yu, Y. (eds) Linked Data and Knowledge Graph. CSWS 2013. Communications in Computer and Information Science, vol 406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54025-7_4
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DOI: https://doi.org/10.1007/978-3-642-54025-7_4
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