Enriching limited information on rare diseases from heterogeneous networks for drug repositioning

Abstract

Background The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father–son information, so it is extremely difficult to predict drugs for the rare disease. Method In this paper, we focus on father–son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What’s more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network. Result Comparing with traditional methods, GCAN makes full use of father–son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning. Conclusion The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

“…From these annotated documents, Watson created a semantic model of the set of RBPs with known mutations that cause ALS, and then applied that model to a candidate set of all other RBPs to cluster all the candidates by similarity to the known set using a graph diffusion algorithm. Gated Recurrent Unit Cooperation-Attention-Network (GCAN) was used in [ 97 ] to predict drugs for rare diseases, with particular attention to Gaucher disease, a rare metabolic disorder in which deficiency of the enzyme glucocerebrosidase results in the accumulation of toxic quantities of certain lipids. Two heterogeneous networks were built for information enhancement; one network contains the father nodes of the rare disease, while the other network contains information on the son nodes.…”

Drug repositioning with gender perspective focused on Adverse Drug Reactions

“…Rare diseases usually pose more challenges to drug repositioning because there is a scarcity of data. Nevertheless, computational approaches have been developed based mainly on similarities between omics data and biochemical features of diseases [18].…”