Rischmüller Karen , Caton Vanessa , Wolfien Markus , Ehlers Luise , van Welzen Matti , Brauer David , Sautter Lea F. , Meyer Fatuma , Valentini Luzia , Wiese Mats L. , Aghdassi Ali A. , Jaster Robert , Wolkenhauer Olaf , Lamprecht Georg , Bej Saptarshi, 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, 2024, https://doi.org/10.3389/fnut.2024.1479501

Umesh, C., Mahendra, M., Bej, S. et al. Challenges and applications in generative AI for clinical tabular data in physiology. Pflugers Arch - Eur J Physiol (2024). https://doi.org/10.1007/s00424-024-03024-w

Srivastava, P., Steuer, A., Ferri, F. et al. Bitter peptide prediction using graph neural networks. J Cheminform 16, 111 (2024). https://doi.org/10.1186/s13321-024-00909-x

Schultz, K.,  Bej, S., Hahn, W., Wolfien, M., Srivastava, P., Wolkenhauer, O.; ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets, Pattern Recognition, Volume 147, 2024, 110138, ISSN 0031-3203, https://doi.org/10.1016/j.patcog.2023.110138

Wacker, Eike Matthias, Bej S. et al., Whole blood RNA sequencing identifies transcriptional differences between primary sclerosing cholangitis and ulcerative colitis, JHEP Reports, Volume 6, Issue 2, 100988, https://doi.org/10.1016/j.jhepr.2023.100988

Bej, S., Umesh, C., Mahendra, M., Schultz, K., Sarkar, J., Wolkenhauer, O., Accounting for diverse feature-types improves patient stratification on tabular clinical datasets}, Machine Learning with Applications, Volume 14, 2023, 100490, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2023.100490

Shettigondahalli Ekanthalu, V., Ender, T., Narra, S., Antwi, E., Bej, S., Nelles, M. Acid leaching of hydrothermally carbonized sewage sludge: Phosphorus release and hydrochar characteristics. Front. Environ. Eng. 2023, Volume 2, https://doi.org/10.3389/fenve.2023.1223247

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

Bej S., Schultz K., Srivastava P., Wolfien M., Wolkenhauer O. A multi-schematic classifier-independent oversampling approach for imbalanced datasets, IEEE Access, vol. 9, pp. 123358-123374, 2021, https://doi.org/10.1109/ACCESS.2021.3108450

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

Srivastava P., Bej S., Schultz K., Yordanova K., Wolkenhauer O. Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts. Biomolecules 2021, 11, 1591 https://doi.org/10.3390/biom11111591

Nguinkal J., Bej S., et al. Comprehensive Characterization of Multitissue Expression Landscape, Co-Expression Networks and Positive Selection in Pikeperch, Cells. 2021; 10(9):2289, https://doi.org/10.3390/cells10092289

Mucha S., Bej S. et al. Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression, Journal of Allergy and Clinical Immunology, 145(4), 1208-1218, 2019, link: https://doi.org/10.1016/j.jaci.2019.10.030

Bej S. Hamiltonian cycles in annular decomposable Barnette graphs, Journal of Discrete Mathematical Sciences and Cryptography online article: https://doi.org/10.1080/09720529.2021.1961893

Bej, S., Steffen, E., Factors of edge-chromatic critical graphs: a brief survey and some equivalences, Lecture Notes of Seminario Interdisciplinare di Matematica, 14, 37-48, 2017, http://dimie.unibas.it/site/home/info/documento3016672.html

Banerjee A., Bej S. On extension of regular graphs, Journal of Discrete Mathematical Sciences and Cryptography, (2018) Vol 21:1, 13-21, https://doi.org10.1080/09720529.2015.1085740,

Kok J., Bej S. Coloring sums of extensions of certain graphs, Journal of Algebra Combinatorics Discrete Structures and Applications, (2016) Vol 5(1) 19-27, http://dx.doi.org/10.13069/jacodesmath.349383

Hahn, W., Schütte, M., Bej, S. et al. Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care. Journal of Personalised Medicine. 2022, 12, 1278. https://doi.org/10.3390/jpm12081278

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

Uellendahl-Werth, F., Maj, C., Bej S.et al. Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease. Commun Biol 5, 80 (2022). https://doi.org/10.1038/s42003-022-03031-6

Srivastava P., Bej S., Schultz K., Yordanova K. and Wolkenhauer O., Attention Retrieval Model for Entity Relation Extraction From Biological Literature, IEEE Access, vol. 10, pp. 22429-22440, 2022, https://doi.org/10.1109/ACCESS.2022.3154820

Bej S., Srivastava P., Wolfien M., Wolkenhauer O. Combining uniform manifold approximation with localized affine shadowsampling improves classification of imbalanced datasets 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8, https://ieeexplore.ieee.org/document/9534072