A Knowledge Graph Completion Method Applied to Literature-Based Discovery for Predicting Missing Links Targeting Cancer Drug Repurposing

Ali Daowd, Samina Abidi, Syed Sibte Raza Abidi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Cancer literature contains a rich body of implicit knowledge which can play an important role in drug repurposing. However, classical knowledge retrieval techniques used in Literature Based Discovery (LBD) suffer from the problem of incomplete knowledge extraction resulting in a large number of knowledge entities being missed. Recently, knowledge graphs (KGs) have been used to represent literature-derived knowledge and support knowledge discovery by representing relations between concepts. Knowledge Graph Completion (KGC) has been proposed as a method to augment knowledge represented as a KG by predicting potential missing relations between concepts in a KG. We posit that KGC methods can be applied to LBD with the goal of augmenting KGs and finding implicit knowledge by reasoning over the KG. In this paper, we present KGC methods (such as FocusE-TransE) to predict missing relations between head and tail entities, rather than the standard head or tail prediction task. Our focus is the generation of a cancer-focused drug repurposing KG, via LBD, replicating recent cancer drug repurposing discoveries. We utilized a time-slicing approach to construct incomplete KGs using semantic triples extracted from cancer literature. Next we apply our KGC methods to augment the base KG, and apply discovery patterns on the augmented KG to generate drug-gene-disease semantic paths that replicate recent cancer drug repurposing discoveries. Further, we assessed the LBD output by comparing drug-disease associations reported in the literature. Our work presents a scalable knowledge discovery framework combining KGC, LBD, and associations measures to discover meaningful implicit knowledge from the literature.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Proceedings
EditorsMartin Michalowski, Syed Sibte Raza Abidi, Samina Abidi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages24-34
Number of pages11
ISBN (Print)9783031093418
DOIs
Publication statusPublished - 2022
Event20th International Conference on Artificial Intelligence in Medicine, AIME 2022 - Halifax, Canada
Duration: Jun 14 2022Jun 17 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13263 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence in Medicine, AIME 2022
Country/TerritoryCanada
CityHalifax
Period6/14/226/17/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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Daowd, A., Abidi, S., & Abidi, S. S. R. (2022). A Knowledge Graph Completion Method Applied to Literature-Based Discovery for Predicting Missing Links Targeting Cancer Drug Repurposing. In M. Michalowski, S. S. R. Abidi, & S. Abidi (Eds.), Artificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Proceedings (pp. 24-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13263 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09342-5_3