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Embedding based knowledge graph link prediction for drug repurposing

- Edoardo Ramalli, M.Sc. student in Computer Science and Engineering

Drug Repurposing is the investigation of existing drugs on the pharmaceutical market for new therapeutic purposes; drug repurposing reduces the time and cost of clinical trial steps, saving years, and billions of dollars in R&D. Identifying new diseases on which a drug can be effective is a complex problem: our approach leverages knowledge graphs (KG), networks composed of many types of entities and relations, on which embedding and graph completion techniques can be applied to infer insights and analyses. Our KG is built from well-known databases such as DrugBank, UniProt, and CTD and contains over one million relationships between more than 70K biological and pharmaceutical entities like diseases, genes, proteins and drugs. In this work, we research the applicability of knowledge graph completion techniques, such as link prediction (and triple classification) using a various number of different embedding models from different families: matrix factorization, geometric and Deep learning. Using these models is possible to infer new drug-disease relationships on our KG, and identify novel drug repurposing candidates. Preliminary experimental results are encouraging and show how state-of-the-art machine learning models, combined with the ever-growing amount of biological data freely available to the research community, could significantly improve the field of drug repurposing.

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Embedding based knowledge graph link prediction for drug repurposing

  1. 1. Ramalli E., Parravicini A., Di Donato G., Salaris M., Santambrogio M. edoardo.ramalli@mail.polimi.it Embedding Based Knowledge Graph Link Prediction for Drug Repurposing 1 July 19, 2020 - vNGC
  2. 2. Breaking News 2 “After Saving his own life with a repurposed drug, a professor reviews every drug being tried against Covid-19. Here’s what he’s found.” By Ryan Prior, CNN June 27, 2020 Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC
  3. 3. Knowledge Graph 3 Proteins Genes Drugs Diseases treat involved interacting parent of associated to encoding interacting Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC
  4. 4. Knowledge Graph 4 Proteins Genes Drugs Diseases treat involved interacting parent of associated to encoding interacting Triples: 183k, Entities: 63k, Relations: 70 32.0% 22.0% 38.0% 8.0% Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC
  5. 5. Link Prediction 5 Disease Drug Protein High Probability Interaction Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC
  6. 6. Embedding 6 0.3 0.1 ... 0.7 0.3 0.1 ... 0.7 0.3 0.1 ... 0.7 Disease Drug Protein Link Prediction Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC
  7. 7. Preliminary Results 7 Accuracy: we are learning H@10 ~ 0.5 → Random baseline has H@10 = 0 Dataset: Better and more data implies greater results → still plenty of open data to integrate Time: 12h e2e KG generation and prediction → dimensionality reduction w.r.t. clinical steps Biological Insight: convergence of ML and better comprehension of pharmaceutical science Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC
  8. 8. Preliminary Results 8 Accuracy: we are learning H@10 ~ 0.5 → Random baseline has H@10 = 0 Dataset: Better and more data implies greater results → still plenty of open data to integrate Time: 12h e2e KG generation and prediction → dimensionality reduction w.r.t. clinical steps Biological Insight: convergence of ML and better comprehension of pharmaceutical science Edoardo Ramalli -- edoardo.ramalli@mail.polimi.it -- July 19, 2020 - vNGC Embedding Based Knowledge Graph Link Prediction for Drug Repurposing July 19, 2020 - vNGC Thank You ! Ramalli E., Parravicini A., Di Donato G., Salaris M., Santambrogio M. edoardo.ramalli@mail.polimi.it

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