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Documents Associated With Scientific Events Year : 2022

Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

Abstract

Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials or by postmarketing pharmacovigilance. However, the molecular mechanisms causing ADRs remain unknown in most cases. This is true even for common toxicities that are classically monitored during trials such as hepatic or skin toxicities. Interestingly, many elements of knowledge about drugs and drug ingredients are available beside clinical trials. In particular, open-access knowledge graphs describe their properties, interactions, and involvements in pathways. Expert classifications have also been manually established by experts and label drugs either as causative or not for several types of ADRs. In our paper, we propose to mine biomedical knowledge graphs to identify biomolecular features that enable to automatically reproduce such expert classifications, distinguishing drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models which explain the classification itself. We also evaluate the assumption that biomolecular features mined from knowledge graphs might provide elements of explanation for the molecular mechanisms behind ADRs.
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Dates and versions

hal-03754898 , version 1 (20-08-2022)

Identifiers

  • HAL Id : hal-03754898 , version 1

Cite

Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Élie Calvier, Ndeye Coumba Ndiaye, et al.. Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining. JOBIM 2022 - Journées Ouvertes en Biologie, Informatique et Mathématiques, Jul 2022, Rennes, France. , JOBIM 2022 Proceedings - Keynotes, Contributed talks, Mini-Symposia. ⟨hal-03754898⟩
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