Ontology and Shared Datasets

This section contains the BRAINTEASER Ontology and Shared Datasets.

During its ideation but, even more, during its execution, the BRAINTEASER project adopted a strong focus on Open Science [1] as a means both to validate its results and methods and to accelerate the transfer of its outcomes. In this respect, the BRAINTEASER project developed its own approach to Open Science consisting of two pillars: the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles [2] for data sharing and the organization of open evaluation challenges.

When it comes to adopting and adhering to FAIR principles, BRAINTEASER developed an ontology to model ALS and MS clinical data, both retrospective and prospective, as well as environmental data, and made the ontology itself available [3] according to FAIR principles.

The BRAINTEASER ontology served the purpose of populating a knowledge base of ALS and MS clinical data, consisting of retrospective and prospective data. The retrospective data amount to about 2,204 ALS patients (static variables, ALSFRS-R questionnaires, spirometry tests, environmental/pollution data) and 1,792 MS patients (static variables, EDSS scores, evoked potentials, relapses, MRIs, environmental/pollution data); the prospective data contain about 86 ALS patients (static variables, ALSFRS-R questionnaires compiled by clinicians or patients using the BRAINTEASER mobile application, sensors data). These datasets have been made available [4] according to FAIR principles as well.

Both the ontology and the ALS and MS clinical datasets have been iteratively developed over the years and they both have been used to fuel the iDPP@CLEF (Intelligent Disease Progression Prediction) [5] open evaluation challenges and have been validated through them. The iDPP@CLEF evaluation challenges, in turn, have generate comparable performance data about the algorithms and methods developed by the research groups participating in the challenges and all these experimental data are available [6] according to FAIR principles.

References

Faggioli, G., Menotti, L., Marchesin, S., Chiò, A., Dagliati, A., de Carvalho, M., Gromicho, M., Manera, U., Tavazzi, E., Di Nunzio, G. M., Silvello, G., and Ferro, N. (2024). An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology. Journal of Biomedical Semantics, 15:16:1-16:28. doi: 10.1186/s13326-024-00317-y

Guazzo, A., Trescato, I., Longato, E., Hazizaj, E., Dosso, D., Faggioli, G., Di Nunzio, G. M., Silvello, G., Vettoretti, M., Tavazzi, E., Roversi, C., Fariselli, P., Madeira, S. C., de Carvalho, M., Gromicho, M., Chiò, A., Manera, U., Dagliati, A., Birolo, G., Aidos, H., Di Camillo, B., and Ferro, N. (2022). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022. In Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Thirteenth International Conference of the CLEF Association (CLEF 2022), pages 395–422. Lecture Notes in Computer Science (LNCS) 13390, Springer, Heidelberg, Germany. doi: 10.1007/978-3-031-13643-6_25

Faggioli, G., Guazzo, A., Marchesin, S., Menotti, L., Trescato, I., Aidos, H., Bergamaschi, R., Birolo, G., Cavalla, P., Chiò, A., Dagliati, A., de Carvalho, M., Di Nunzio, G. M., Fariselli, P., Garcia Dominguez, J. M., Gromicho, M., Longato, E., Madeira, S. C., Manera, U., Silvello, G., Tavazzi, E., Tavazzi, E., Vettoretti, M., Di Camillo, B., and Ferro, N. (2023). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023. In Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Fourteenth International Conference of the CLEF Association (CLEF 2023), pages 343-369. Lecture Notes in Computer Science (LNCS) 14163, Springer, Heidelberg, Germany. doi: 10.1007/978-3-031-42448-9_24

Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., Chiò, A., Dagliati, A., de Carvalho, M., Di Nunzio, G. M., Fariselli, P., Garcia Dominguez, J. M., Gromicho, M., Guazzo, A., Longato, E., Madeira, S., Manera, U., Marchesin, S., Menotti, L., Silvello, G., Tavazzi, E., Tavazzi, E., Trescato, I., Vettoretti, M., Di Camillo, B., and Ferro, N. (2024). Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024. In Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Fifteenth International Conference of the CLEF Association (CLEF 2024) – Part II, pages 118-139. Lecture Notes in Computer Science (LNCS) 14959, Springer, Heidelberg, Germany. doi: 10.1007/978-3-031-71908-0_6

Chiò A, Mora G, Moglia C, Manera U, Canosa A, Cammarosano S, Ilardi A, Bertuzzo D, Bersano E, Cugnasco P, Grassano M, Pisano F, Mazzini L, Calvo A (2017). Piemonte and Valle d’Aosta Register for ALS (PARALS). Secular Trends of Amyotrophic Lateral Sclerosis: The Piemonte and Valle d’Aosta Register. JAMA Neurol., 74(9):1097-1104. doi: 10.1001/jamaneurol.2017.1387

Bergamaschi R, Monti MC, Trivelli L, Mallucci G, Gerosa L, Pisoni E, Montomoli C. (2021). PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach. Environ Sci Pollut Res Int., 28(3):2804-2809, doi: 10.1007/s11356-020-10595-5

Bergamaschi R, Monti MC, Trivelli L, Introcaso VP, Mallucci G, Borrelli P, Gerosa L, Montomoli C. (2020). Increased prevalence of multiple sclerosis and clusters of different disease risk in Northern Italy. Neurol Sci., 41(5):1089-1095, doi: 10.1007/s10072-019-04205-7

Alves, I., Gromicho, M., Oliveira Santos, M., Pinto, S., Pronto-Laborinho, A,, Swash, M., and de Carvalho, M. (2023) Demographic changes in a large motor neuron disease cohort in Portugal: a 27 year experience. Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 24(7–8), 614–624. doi: 10.1080/21678421.2023.2220747.

[1] https://www.unesco.org/en/open-science

[2] https://www.go-fair.org/fair-principles/

[3] https://doi.org/10.5281/zenodo.12789731

[4] https://doi.org/10.5281/zenodo.12789962

[5] https://brainteaser.health/open-evaluation-challenges/

[6] iDPP@CLEF 2022: https://doi.org/10.5281/zenodo.7477919

iDPP@CLEF 2023: https://doi.org/10.5281/zenodo.10210125

iDPP@CLEF 2024: https://zenodo.org/records/14030410