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Predicting drug-disease associations with heterogeneous network embedding.

Kai Yang1, Xingzhong Zhao1, David Waxman1

  • 1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, People's Republic of China.

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This study introduces HED, a novel method for predicting drug-disease associations using heterogeneous network embedding. HED accurately identifies potential new uses for existing drugs, advancing precision medicine.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Network science

Background:

  • Drug-disease association prediction is crucial for precision medicine and drug repurposing.
  • These associations can be modeled as complex heterogeneous networks.

Purpose of the Study:

  • To propose a novel method, HED (Heterogeneous network Embedding for Drug-disease association), for predicting potential drug-disease associations.
  • To leverage network embedding techniques for characterizing these associations within a heterogeneous network.

Main Methods:

  • Constructing a heterogeneous network from known drug-disease associations.
  • Employing network embedding to represent drug and disease nodes and their relationships.
  • Training a classifier on the embedded network to predict novel associations.

Main Results:

  • HED demonstrated superior performance compared to existing methods on two real-world datasets.
  • Several predicted drug-disease associations were validated through literature evidence.
  • For example, HED predicted carvedilol for atrial fibrillation, later supported by clinical trials.

Conclusions:

  • HED is an effective approach for predicting drug-disease associations.
  • The method holds promise for identifying new therapeutic indications for existing drugs.
  • This contributes to the advancement of precision medicine through big data analysis.