=Paper=
{{Paper
|id=Vol-3726/keynote1
|storemode=property
|title=Towards Biomedical Neurosymbolic AI: From Knowledge Infrastructure to Explainable Predictions
|pdfUrl=https://ceur-ws.org/Vol-3726/keynote1.pdf
|volume=Vol-3726
|authors=Michel Dumontier
|dblpUrl=https://dblp.org/rec/conf/sewebmeda/Dumontier24
}}
==Towards Biomedical Neurosymbolic AI: From Knowledge Infrastructure to Explainable Predictions==
<pdf width="1500px">https://ceur-ws.org/Vol-3726/keynote1.pdf</pdf>
<pre>
                                Keynote: Towards Biomedical Neurosymbolic AI:
                                From Knowledge Infrastructure to Explainable
                                Predictions
                                Michel Dumontier1
                                1
                                    Maastricht University, Maastricht, Limburg, NL


                                                                         Abstract
                                                                         The increased availability of biomedical data, particularly in the public domain, offers the opportunity
                                                                         to better understand human health and to develop effective therapeutics for a wide range of unmet
                                                                         medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to
                                                                         productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or
                                                                         incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly
                                                                         restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on
                                                                         how data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles.
                                                                         Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into
                                                                         predictive models for a wide range of biomedical applications, these models often fail even when the
                                                                         correct answer is already known, and fail to explain individual predictions in terms that data scientists
                                                                         can appreciate. These limitations suggest that new methods to produce practical artificial intelligence
                                                                         are still needed.
                                                                             In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare
                                                                         FAIR and ”AI-ready” data and services along with (2) neurosymbolic AI methods to improve the quality
                                                                         of predictions and to generate plausible explanations. Attention is given to standards, platforms, and
                                                                         methods to wrangle knowledge into simple, but effective semantic and latent representations, and to
                                                                         make these available into standards-compliant and discoverable interfaces that can be used in model
                                                                         building, validation, and explanation. Our work, and those of others in the field, create a baseline for
                                                                         building trustworthy and easy-to-deploy AI models in biomedicine.




                                SeWebMeDa-2024: 7th International Workshop on Semantic Web solutions for large-scale biomedical data analytics,
                                May 26, 2024, Hersonissos, Greece
                                Envelope-Open michel.dumontier@maastrichtuniversity.nl (M. Dumontier)
                                GLOBE https://www.maastrichtuniversity.nl/mj-dumontier (M. Dumontier)
                                Orcid 0000-0003-4727-9435 (M. Dumontier)
                                                                       © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
                                    CEUR
                                    Workshop
                                    Proceedings
                                                  http://ceur-ws.org
                                                  ISSN 1613-0073
                                                                       CEUR Workshop Proceedings (CEUR-WS.org)




CEUR
                  ceur-ws.org
Workshop      ISSN 1613-0073
Proceedings

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