Knowledge Graph Convolutional Network with Heuristic Search for Drug Repositioning.

Drug repositioning is a strategy of repurposing approved drugs for treating new indications, which can accelerate the drug discovery process, reduce development costs, and lower the safety risk. The advancement of biotechnology has significantly accelerated the speed and scale of biological data generation, offering significant potential for drug repositioning through biomedical knowledge graphs that integrate diverse entities and relations from various biomedical sources. To fully learn the semantic information and topological structure information from the biological knowledge graph, we propose a knowledge graph convolutional network with a heuristic search, named KGCNH, which can effectively utilize the diversity of entities and relationships in biological knowledge graphs, as well as topological structure information, to predict the associations between drugs and diseases.

DRGCL: Drug Repositioning via Semantic-enriched Graph Contrastive Learning.

Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted remarkable attention, which can narrow down repositioning candidates. Recently, graph neural networks (GNNs) have been widely used and achieved promising results in drug repositioning.

Drug repositioning with adaptive graph convolutional networks.

Motivation: Drug repositioning is an effective strategy to identify new indications for existing drugs, providing the quickest possible transition from bench to bedside. With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs based methods exist limitations in deeply integrating node features and topological structures, which may hinder the capability of GCNs.

Partner-Specific Drug Repositioning Approach Based on Graph Convolutional Network.

Drug repositioning identifies novel therapeutic potentials for existing drugs and is considered an attractive approach due to the opportunity for reduced development timelines and overall costs. Prior computational methods usually learned a drug's representation from an entire graph of drug-disease associations. Therefore, the representation of learned drugs representation are static and agnostic to various diseases.

The Warsaw breakage syndrome-related protein DDX11 is required for ribosomal RNA synthesis and embryonic development.

DDX11 was recently identified as a cause of Warsaw breakage syndrome (WABS). However, the functional mechanism of DDX11 and the contribution of clinically described mutations to the pathogenesis of WABS are elusive. Here, we show that DDX11 is a novel nucleolar protein that preferentially binds to hypomethylated active ribosomal DNA (rDNA) gene loci, where it interacts with upstream binding factor (UBF) and the RNA polymerase I (Pol I).