Authors:
Martin Drancé
;
Marina Boudin
;
Fleur Mougin
and
Gayo Diallo
Affiliation:
Inserm U1219, Bordeaux Population Health Research Center, Team ERIAS, University of Bordeaux, France
Keyword(s):
Artificial Intelligence, XAI, Drug Repurposing, Knowledge Graph, Bioinformatics.
Abstract:
Today in the health domain, the challenge is to build a more transparent artificial intelligence, less affected by the opacity intrinsic to the mathematical concepts it uses. Among the fields which use AI techniques, is drug development, and more specifically drug repurposing. DR involves finding a new indication for an existing drug. The hypotheses generated by DR techniques must be validated. Therefore, the mechanism of generation must be understood. In this paper, we describe the use of a state-of-the-art neuro-symbolic algorithm in order to explain the process of link prediction in a knowledge graph-based computational drug repurposing. Link prediction consists of generating hypotheses about the relationships between a known molecule and a given target. More specifically, the implemented approach allows to understand how the organization of data in a knowledge graph changes the quality of predictions.