Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses.
We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional...
| Publicado en: | Journal of Biomedical Informatics Vol. 119 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Jul2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151195305&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151195305 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Jul2021 vid: 119 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 151195305 151195305 NLM34119691 151195305 10.1016/j.jbi.2021.103838 NLM34119691 151195305 ppct: 1 formats: tig: atl: Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses. aug: au: Moon, Changsung Jin, Chunming Dong, Xialan Abrar, Saad Zheng, Weifan Chirkova, Rada Y. Tropsha, Alexander affil: Department of Computer Science, North Carolina State University, Raleigh, NC 27695, USA sug: subj: Drugs Prescriptions, Drug Algorithms Human Information Science Knowledge Comparative Studies Multicenter Studies Evaluation Research Validation Studies Multidimensional Health Locus of Control Scales Funding Source ab: We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional vector and the predicate is regarded as a translation vector from a subject to an object vectors. These vectors are optimized so that when a subject-predicate-object triple represents a known drug-disease-target relationship, the summed vector between the subject and the predicate is to be close to that of the object; otherwise, the summed vector is distant from the object. The DTINet dataset was utilized to test this algorithm and discover unknown links between drugs and diseases. In cross-validation experiments, this new algorithm outperformed the original DTINet model. The MRR (Mean Reciprocal Rank) values of our models were around 0.80 while those of the original model were about 0.70. In addition, we have identified and verified several pairs of new therapeutic relations as well as adverse effect relations that were not recorded in the original DTINet dataset. This approach showed excellent performance, and the predicted drug-disease and drug-side-effect relationships were found to be consistent with literature reports. This novel method can be used to analyze diverse types of emerging biomedical and healthcare-related knowledge graphs (KG). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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