Selected article for: "computational approach and drug repurposing"

Author: Liu, Yushan; Hildebrandt, Marcel; Joblin, Mitchell; Ringsquandl, Martin; Raissouni, Rime; Tresp, Volker
Title: Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
  • Cord-id: b43u6gvj
  • Document date: 2021_3_18
  • ID: b43u6gvj
    Snippet: Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems. The nature of biological data leads to a graph structure that differs from those typically encountered in benchmarking datasets. To understand the implications this may have on the performance of reasoning algorithms, we conduct an empirical study based on the real-world task of drug repurposing. We formulate this task as a link prediction problem where both compounds and diseases corr
    Document: Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems. The nature of biological data leads to a graph structure that differs from those typically encountered in benchmarking datasets. To understand the implications this may have on the performance of reasoning algorithms, we conduct an empirical study based on the real-world task of drug repurposing. We formulate this task as a link prediction problem where both compounds and diseases correspond to entities in a knowledge graph. To overcome apparent weaknesses of existing algorithms, we propose a new method, PoLo, that combines policy-guided walks based on reinforcement learning with logical rules. These rules are integrated into the algorithm by using a novel reward function. We apply our method to Hetionet, which integrates biomedical information from 29 prominent bioinformatics databases. Our experiments show that our approach outperforms several state-of-the-art methods for link prediction while providing interpretability.

    Search related documents:
    Co phrase search for related documents
    • Try single phrases listed below for: 1