Umesh, C., Schultz, K., Mahendra, M., Bej, S., & Wolkenhauer, O. (2025). Preserving logical and functional dependencies in synthetic tabular data. Pattern Recognition, 163, 111459. https://doi.org/10.1016/j.patcog.2025.111459

Schultz, K., Bej, S., Hahn, W., Wolfien, M., Srivastava, P., & Wolkenhauer, O. (2024). ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, 147, 110138. https://doi.org/10.1016/j.patcog.2023.110138

Bordoloi, R., Réda, C., Trautmann, O., Bej, S., & Wolkenhauer, O. (2025). Multivariate functional linear discriminant analysis for partially-observed time series. Machine Learning, 114(3), 80. https://doi.org/10.1007/s10994-025-06741-0

Bej, S., Davtyan, N., Wolfien, M., Nassar, M., Wolkenhauer, O.  LoRAS: An oversampling approach for imbalanced dataset, Mach Learn vol 110, 279–301 (2021). https://doi.org/10.1007/s10994-020-05913-4

Tejaswi, K., Vikas, M., Praharsha, H., Mandal, P., Chakraborty, S., Wolkenhauer, O., & Bej, S. (2025). Detection of pre-ictal epileptic events using a self-attention based neural network from raw Neonatal EEG data. Computers in Biology and Medicine, 195, 110518. https://doi.org/10.1016/j.compbiomed.2025.110518

Bej S., Galow A-M., David R., Wolfien M., Wolkenhauer O. Automated annotation of rare-cell  types from single-cell RNA-sequencing data through synthetic oversampling, BMC Bioinformatics 22, 557 (2021), https://doi.org/10.1186/s12859-021-04469-x

Uellendahl-Werth, F., Maj, C., Borisov, O., Juzenas, S., Wacker, E. M., Jørgensen, I. F., Steiert, T. A., Bej, S., Krawitz, P., Hoffmann, P., Schramm, C., Wolkenhauer, O., Banasik, K., Brunak, S., Schreiber, S., Karlsen, T. H., Degenhardt, F., Nöthen, M., Franke, A., … Ellinghaus, D. (2022). Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease. Communications Biology, 5(1), 1–15. https://doi.org/10.1038/s42003-022-03031-6

Rischmüller, K., Caton, V., Wolfien, M., Ehlers, L., van Welzen, M., Brauer, D., Sautter, L. F., Meyer, F., Valentini, L., Wiese, M. L., Aghdassi, A. A., Jaster, R., Wolkenhauer, O., Lamprecht, G., & Bej, S. (2024). Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters. Frontiers in Nutrition, 11. https://doi.org/10.3389/fnut.2024.1479501

Bej, S., Sarkar, J., Biswas, S. et al. Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach. Nutr. Diabetes 12, 27 (2022). https://doi.org/10.1038/s41387-022-00206-2