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

Résultat de recherche: Conference contribution

7 Citations (Scopus)

Résumé

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.

Langue d'origineEnglish
Titre de la publication principaleArtificial Intelligence in Medicine - 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Proceedings
ÉditeursMartin Michalowski, Syed Sibte Raza Abidi, Samina Abidi
Maison d'éditionSpringer Science and Business Media Deutschland GmbH
Pages24-34
Nombre de pages11
ISBN (imprimé)9783031093418
DOI
Statut de publicationPublished - 2022
Événement20th International Conference on Artificial Intelligence in Medicine, AIME 2022 - Halifax, Canada
Durée: juin 14 2022juin 17 2022

Séries de publication

PrénomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13263 LNAI
ISSN (imprimé)0302-9743
ISSN (électronique)1611-3349

Conference

Conference20th International Conference on Artificial Intelligence in Medicine, AIME 2022
Pays/TerritoireCanada
VilleHalifax
Période6/14/226/17/22

Note bibliographique

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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