![](bg6.png)
Table 4: Comparison of the metrics obtained for drug repositioning through the is substance that treats relation on the
OREGANO KG with the results obtained on Hetionet.
Data MRR Hits@1 Hits@3 Hits@10
OREGANO 0.4980 0.3898 0.5551 0.7300
HETIONET 0.4300 0.3370 0.4700 0.6410
5 CONCLUSION
In this paper, we have described a neuro-symbolic
XAI solution applied to the DR task using a KG. The
study has been implemented and evaluated in the con-
text of the OREGANO project using a state-of-the-art
algorithm which enables the explainability. Results
equivalent to those of the state of the art were obtained,
as well as several ways to provide an explanation for
each prediction, i.e. intrinsic to the model’s opera-
tion, but also post-hoc in a mechanistic way using fea-
tures of a graph oriented database. The challenges sur-
rounding this work are numerous. First, it is important
to better understand how the organization of data in a
KG affects the prediction task. This will be partic-
ularly important for the application of these methods
to DR for rare diseases, where data is by definition
less abundant. Also, more flexible methods must be
thought of, allowing biochemists and other clinicians
to easily participate in the learning process, bringing
their knowledge to the sum of data available for DR.
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