Drug repositioning (DR) identifies new therapeutic uses for approved drugs, reducing development burdens and offering safer treatment options for patients. While high-throughput technologies generate complex, large-scale multiomics data, existing DR tools struggle to comprehensively analyze the resulting biological networks. To address this challenge, we present DRHIN, an integrated, interactive web server for DR over heterogeneous information networks (HINs) using advanced deep learning techniques. DRHIN integrates transcriptomics, proteomics, and microbiome data, incorporating eight biological entities and 19 association types to build diverse HINs and elucidate the underlying molecular mechanisms. It includes 19 state-of-the-art graph representation algorithms, enabling flexible training, comparison, and evaluation of heterogeneous network data. The platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible. Leveraging high-performance computing, DRHIN efficiently processes million-scale networks, ensuring practical applicability in real-world scenarios. The web server is freely accessible at http://drhin.tianshanzw.cn.
Bowei Zhao, Dongxu Li, Yue Yang et al.· Journal of Chemical Informat...· 6 citations· ⚡1
Graph clustering aims to group nodes into meaningful clusters, a crucial task for understanding the behavior of complex networks and their underlying structural organization. Leveraging the expressive power of deep learning, graph neural networks (GNNs) have emerged as popular tools for performing graph clustering with high accuracy. However, despite their promise, existing GNN-based approaches often lack structural reasoning mechanisms to adaptively infer and optimize each node's receptive field in a task-specific manner, limiting their potential to further improve both accuracy and interpretability. In this paper, we propose a novel reinforced structural reasoning framework, termed RGIGC, for interpretable graph clustering. RGIGC formulates receptive field configuration as a structural reasoning problem and employs reinforcement learning with two Q-learning agents to infer and optimize the structural receptive field of each node. These agents are guided by reward criteria that strategically shape the clustering process to achieve desired outcomes. By identifying critical structures of receptive fields that capture the intrinsic features of nodes, RGIGC can enhance the interpretability of the resulting clusters. Extensive experiments on eight real-world graph datasets of varying scales have been conducted, and the results demonstrate that RGIGC outperforms state-of-the-art graph clustering algorithms in both accuracy and interpretability.
Yue Yang, Dongxu Li, Hengchuang Yin et al.· Proceedings of the 32nd ACM...· 0 citations