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Paper   IPM / Biological Sciences / 17575
School of Biological Sciences
  Title:   DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing
1.  Zahra Ghorbanali
2.  Fatemeh Zare-Mirakabad
3.  Najmeh Salehi
4.  Mohammad Akbari
5.  Ali Masoudi-Nejad
  Status:   Published
  Journal: BMC Bioinformatics
  Year:  2023
  Supported by:  IPM
This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics.

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