IUS Publications

Found 6 results
Filters: Author is Devaux, Yvan  [Clear All Filters]
2025
S. Bezzina Wettinger, Karaduzovic-Hadziabdic, K., Attard, R., Farrugia, R., Wolford, B. N., Chierici, M., Jurman, G., Alexiou, P., Peñalvo, J. L., Costa, R. S., Basílio, J., Sabovčik, F., Vitorino, R., Schmid, J. A., Shigdel, R., Vilne, B., Hatzigeorgiou, A. G., Sopić, M., Devaux, Y., Magni, P., Tellez-Plaza, M., Kreil, D. P., and Gruca, A., “Bottlenecks in advancing and applying multiomic data integration—common data resources as rate-limiting drivers—the high-impact use case of atherosclerotic cardiovascular diseaseAbstract”, Briefings in Bioinformatics, vol. 26, no. 5, 2025.
2024
Y. Devaux, Zhang, L., Lumley, A. I., Kanita, K., Mooser, V., Rousseau, S., Shoaib, M., Satagopam, V., Adilović, M., Srivastava, P. Kumar, Emanueli, C., Martelli, F., Greco, S., Badimon, L., Padro, T., Lustrek, M., Scholz, M., Rosolowski, M., Jordan, M., Brandenburger, T., Benczik, B., Ágg, B., Ferdinandy, P., Vehreschild, J. Janne, Lorenz-Depiereux, B., Dörr, M., Witzke, O., Sanchez, G., Kul, S., Baker, A. H., Fagherazzi, G., Ollert, M., Wereski, R., Mills, N. L., and Firat, H., “Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortalityAbstract”, Nature Communications, vol. 15, no. 1, 2024.
, Adilovic, M., Zhang, L., and Devaux, Y., “Prediction of COVID‐19 severity using machine learning”, Clinical and translational medicine, 2024.
2023
M. Sopić, Karađuzović-Hadžiabdić, K., Kardassis, D., Maegdefessel, L., Martelli, F., Meerson, A., Munjas, J., Niculescu, L. S., Stoll, M., Magni, P., and Devaux, Y., “Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges”, Journal of Molecular and Cellular Cardiology Plus, vol. 6, p. 100048, 2023.
2021
L. Badimon, Robinson, E. L., Jusic, A., Carpusca, I., deWindt, L. J., Emanueli, C., Ferdinandy, P., Gu, W., Gyöngyösi, M., Hackl, M., Karađuzović-Hadžiabdić, K., Lustrek, M., Martelli, F., Nham, E., Potočnjak, I., Satagopam, V., Schneider, R., Thum, T., and Devaux, Y., “Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129Abstract”, Cardiovascular Research, vol. 117, no. 8, pp. 1823 - 1840, 2021.
2019
C. Gomes, Ágg, B., Andova, A., Arslan, S., Baker, A., Barteková, M., Beis, D., Betsou, F., Wettinger, S., Bugarski, B., Condorelli, G., Silva, G., Danilin, S., Gonzalo-Calvo, D., Buil, A., Carmo-Fonseca, M., Enguita, F., Felekkis, K., Ferdinandy, P., Gyöngyösi, M., Hackl, M., Karađuzović-Hadžiabdić, K., Hellemans, J., Heymans, S., Hlavackova, M., Hoydal, M., Jankovic, A., Jusic, A., Kardassis, D., Kerkelä, R., Kuster, G., Lakkisto, P., Leszek, P., Lustrek, M., Maegdefessel, L., Martelli, F., Novella, S., O’Brien, T., Papaneophytou, C., Pedrazzini, T., Pinet, F., Popescu, O., Potočnjak, I., Robinson, E., Sasson, S., Scholz, M., Simionescu, M., Stoll, M., Varga, Z., Vinciguerra, M., Xuereb, A., Yilmaz, M., Emanueli, C., Devaux, Y., and CAonesevenonetwonine), onbehalf of, “Catalyzing Transcriptomics Research in Cardiovascular Disease: The CardioRNA COST Action CA17129”, Non-Coding RNA, vol. 5, no. 2, p. 31, 2019.