Prof. Jean-Daniel Zucker

Senior Researcher / Directeur de Recherche, IRD
Professor, Sorbonne Université and Paris Dauphine-PSL, France

Email: [email protected]

Qualifications

Ph.D. in Machine Learning, Université Paris VI / Sorbonne Université, France.

M.Sc. in Artificial Intelligence, Université Paris 5 and Université Paris VI, France.

Publications (Selected)

1. Adriouch, S., Belda, E., Swartz, T. D., et al. (2025). Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes. Gut Microbes, 17(1), Article 2599565.
2. Lence, A., Fall, A., Cohen, S. D., et al. (2026). ECGTIzER: An open-source, fully automated pipeline for digitization and signal recovery from paper electrocardiograms. Biomedical Signal Processing and Control, 112, Article 108710.
3. Ariouat, H., Sklab, Y., Prifti, E., et al. (2025). Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation. Applications in Plant Sciences, 13(2), Article e70000.
4. Steinbach, E., Belda, E., Alili, R., et al. (2024). Comparative analysis of the duodenojejunal microbiome with the oral and fecal microbiomes reveals its stronger association with obesity and nutrition. Gut Microbes, 16(1), Article 2405547.
5. Belda, E., Capeau, J., Zucker, J. D., et al. (2024). Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs. BMC Medical Genomics, 17(1), Article 209.
6. Surabattula, R., Lassen, P. B., Myneni, S. R., et al. (2024). Serum thrombospondin-2 and insulin -like growth factor binding protein 7 predict liver fibrosis and fibrosis regression in patients with MASLD post bariatric surgery. Journal of Hepatology, 80(Suppl. 1), S585–S585.
7. Surabattula, R., Lassen, P. B., Myneni, S. R., et al. (2024). SERUM THROMBOSPONDIN (TSP2) AND INSULIN-LIKE GROWTH FACTOR BINDING PROTEIN 7 (IGFBP7) PREDICT LIVER FIBROSIS, FIBROGENESIS AND REGRESSION IN MAFLD PATIENTS UNDERGOING BARIATRIC SURGERY. Gastroenterology, 166(5, Suppl. S), S1633–S1633.
8. Roy, G., Prifti, E., Belda, E., et al. (2024). Deep learning methods in metagenomics: a review. Microbial Genomics, 10(4), Article 001231.
9. Dash, N. R., Al Bataineh, M. T., Alili, R., et al. (2023). Functional alterations and predictive capacity of gut microbiome in type 2 diabetes. Scientific Reports, 13(1), Article 22386.
10. Surabattula, R., Lassen, P. B., Myneni, S., et al. (2023). NOVEL MATRICELLULAR SERUM MARKERS PREDICT LIVER FIBROSIS AND FIBROGENESIS IN PATIENTS WITH NAFLD UNDERGOING BARIATRIC SURGERY. Hepatology, 78(Suppl. 1), S854–S854.
11. Andrikopoulos, P., Aron-Wisnewsky, J., Chakaroun, R., et al. (2023). Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide. Nature Communications, 14(1), Article 5843.
12. Adriouch, S., Belda, E., Prifti, E., et al. (2023). Lifestyle profiles are linked to gut microbiota for different cardiometabolic profiles in the MetaCardis study. Diabetologia, 66(Suppl. 1), S170–S170.
13. Lence, A., Extramiana, F., Fall, A., et al. (2023). Automatic digitization of paper electrocardiograms-A systematic review. Journal of Electrocardiology, 80, 125–132.
14. Debédat, J., Le Roy, T., Voland, L., et al. (2022). The human gut microbiota contributes to type-2 diabetes non-resolution 5-years after Roux-en-Y gastric bypass. Gut Microbes, 14(1), Article 2050635.
15. Martenot, V., Masdeu, V., Cupe, J., et al. (2022). LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning. BMC Medical Informatics and Decision Making, 22(1), Article 202.
16. Chapuis, K., Pham, M. D., Brugière, A., et al. (2022). Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations. International Journal of Geographical Information Science, 36(9), 1741–1783.
17. Fromentin, S., Forslund, S. K., Chechi, K., et al. (2022). Microbiome and metabolome features of the cardiometabolic disease spectrum. Nature Medicine, 28(2), 303–309.
18. Belda, E., Voland, L., Tremaroli, V., et al. (2022). Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism. Gut, 71(12), 2463–2480.
19. Alili, R., Belda, E., Fabre, O., et al. (2022). Characterization of the Gut Microbiota in Individuals with Overweight or Obesity during a Real-World Weight Loss Dietary Program: A Focus on the Bacteroides 2 Enterotype. Biomedicines, 10(1), Article 16.

Profile Details
https://jdzucker.github.io/
https://scholar.google.com/citations?user=bcrbZrEAAAAJ&hl=en
https://www.researchgate.net/profile/Jean-Daniel-Zucker

WOS ResearcherID: K-3008-2016