Wetenschappelijke publicaties

Teunissen, C. E., Kimble, L., Bayoumy, S., Bolsewig, K., Burtscher, F., Coppens, S., Das, S., Gogishvili, D., Gomes, B. F., de San José, N. G., Mavrina, E., Meda, F. J., Mohaupt, P., Mravinacová, S., Waury, K., Wojdała, A. L., Abeln, S., Chiasserini, D., Hirtz, C., ... Zetterberg, H. (2023). Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias. Molecular and Cellular Proteomics, 22(10), Article 100629.
Bridel, C., Gils, J. H. M. V., Miedema, S. S. M., Hoozemans, J. J. M., Pijnenburg, Y. A. L., Smit, A. B., Rozemuller, A. J. M., Abeln, S., & Teunissen, C. E. (2023). Clusters of co-abundant proteins in the brain cortex associated with fronto-temporal lobar degeneration. Alzheimer's Research and Therapy, 15(1), Article 59.
Gogishvili, D., Vromen, E. M., Hertog, S. K., Lemstra, A. W., Pijnenburg, Y. A. L., Visser, P. J., Tijms, B. M., Campo, M. D., Abeln, S., Teunissen, C. E., & Vermunt, L. (2023). Discovery of novel CSF biomarkers to predict progression in dementia using machine learning. Scientific Reports, 13(1), Article 6531.
Waury, K., de WIt, R., Verberk, IMW., Teunissen, CE., & Abeln, S. (2023). Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery. Journal of Proteome Research, 22(9), 3068-3080.


Wetenschappelijke publicaties

Crusoe, M. R., Abeln, S., Iosup, A., Amstutz, P., Chilton, J., Tijanić, N., Ménager, H., Soiland-Reyes, S., & Goble, C. (2022). Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language. Communications of the ACM, 65(6), 54-63.
Gils, J. H. M. V., Gogishvili, D., Eck, J. V., Bouwmeester, R., Dijk, E. V., Abeln, S., & Gromiha, M. (Ed.) (2022). How sticky are our proteins? Quantifying hydrophobicity of the human proteome. Bioinformatics Advances, 2(1).
Capel, H., Feenstra, A., & Abeln, S. (2022). Multi-task learning to leverage partially annotated data for PPI interface prediction. Scientific Reports, 12.


Wetenschappelijke publicaties

Gils, J. H. M. V., Dijk, E. V., Peduzzo, A., Hofmann, A., Vettore, N., Schützmann, M. P., Groth, G., Mouhib, H., Otzen, D. E., Buell, A. K., & Abeln, S. (2020). The hydrophobic effect characterises the thermodynamic signature of amyloid fibril growth. PLoS Computational Biology, 16(5).