Systematic integration of biomedical knowledge prioritizes drugs for repurposing

By Daniel S. Himmelstein, A. Lizee, C. Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, A. Green, Pouya Khankhanian, Sergio E. Baranzini. bioRxiv, 2017

633 citations recorded by the source corpus.

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1758eeac1b000571ee1eaf425abea72fc1ec6e9b

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Citations in the corpus

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings (2019, background): “SPOKE is a heterogeneous knowledge network that includes data from 29 publicly available databases, representing a signi ﬁ cant proportion of information gathered over ﬁ ve decades of biomedical research 12 .”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, background): “Facts # Test Facts ATOMIC [205] 304,388 9 610,536 87,700 87,701 Countries [206] 271 2 1,110 24 24 CoDEX-S [207] 2,034 42 32,888 3,654 3656 CoDEX-M [207] 17,050 51 185,584 20620 20622 CoDEX-L [207] 77,951 69 551,193 30,622 30622 ConceptNet [208] 28,370,083 50 27,259,933 3,407,492 3,407,492 ConceptNet”
Building a knowledge graph to enable precision medicine (2022, background): “For example, the SPOKE network is a seminal effort that linked 29 heterogeneous biomedical databases to build a disease-centric knowledge graph 37 .”