Hierarchical and Centrality driven Polypharmacy Side Effect Prediction Model

Abubakhari Sserwadda, Alper Ozcan, Yusuf Yaslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Modelling polypharmacy side effect (POSE) prediction as a graph link prediction problem eases the adaptation of the convention graph theory and geometric learning approaches to predicting polypharmacy side effects. However, popular methods often deployed on POSE prediction concentrate on capturing the local neighborhood information neglecting other rich network information. Among the under-looked information includes the centrality role of nodes in the network and the network hierarchical information. In this work, we propose a novel architecture that preserves both the Hierarchical and node Centrality role during embedding learning for POSE prediction (HC-POSE). Firstly, we exploit the underlying network hier-archical information with k-core decomposition. Secondly, we preserve the node centrality and topology information with node strength-based features. Lastly, we propose an end-to-end archi-tecture that preserves all the crucial diverse network information in a unified framework. From the experimental results, HC-POSE showed a 3% improvement in POSE prediction accuracy over the best baseline.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages384-387
Number of pages4
ISBN (Electronic)9781665470100
DOIs
Publication statusPublished - 2022
Event7th International Conference on Computer Science and Engineering, UBMK 2022 - Diyarbakir, Turkey
Duration: 14 Sept 202216 Sept 2022

Publication series

NameProceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

Conference

Conference7th International Conference on Computer Science and Engineering, UBMK 2022
Country/TerritoryTurkey
CityDiyarbakir
Period14/09/2216/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Centrality
  • Hierarchical Information
  • K-core decomposition
  • Node Strength

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