Abstract
In the existing genetic similarity search algorithm based on meta-path, the accuracy of genetic similarity calculation results is low because the implicit correlation between genes, diseases and other related factors is not taken into account. To solve this problem, an improved weighted meta-path genetic similarity search algorithm gSim-Search is proposed. This algorithm uses binary network to spread resources. It not only reconstructs the relationship between nodes in gene-disease-phenotype heterogeneous networks, but also assigns reasonable weights to the relationship, to express the degree of correlation of nodes and reflect the similarity of genes scientifically. It solves the problem of sparse connection and insufficient information in traditional metapath-based methods. Experiments show that the algorithm greatly improves the accuracy of predicting genetic similarity between breast cancer and obesity.
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Du, J., Yang, K., Jin, X. (2020). Research on Gene Similarity Search Algorithm in Heterogeneous Network. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_30
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DOI: https://doi.org/10.1007/978-981-15-3250-4_30
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