A selection of our most recent publications.

Gogishvili, D., Vromen, E. M., Koppes-den Hertog, S., Lemstra, A. W., Pijnenburg, Y. A. L., Visser, P. J., Tijms, B. M., Del Campo, M., 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), 6531.

Bridel, C., van Gils, J. H. M., 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 & therapy, 15(1), 59. 

Bosdriesz, E., Fernandes Neto, J. M., Sieber, A., Bernards, R., Blüthgen, N., & Wessels, L. F. A. (2022). Identifying mutant-specific multi-drug combinations using comparative network reconstruction. iScience, 25(8), 104760.

Lakbir, S., Lahoz, S., Cuatrecasas, M., Camps, J., Glas, R. A., Heringa, J., Meijer, G. A., Abeln, S., & Fijneman, R. J. A. (2022). Tumour break load is a biologically relevant feature of genomic instability with prognostic value in colorectal cancer. European journal of cancer (Oxford, England : 1990), 177, 94–102. 

Waury, K., Willemse, E. A. J., Vanmechelen, E., Zetterberg, H., Teunissen, C. E., & Abeln, S. (2022). Bioinformatics tools and data resources for assay development of fluid protein biomarkers. Biomarker research, 10(1), 83. 

Hou, Q., Waury, K., Gogishvili, D., & Feenstra, K. A. (2022). Ten quick tips for sequence-based prediction of protein properties using machine learning. PLOS Computational Biology, 18(12). 

van Gils, J. H. M., Gogishvili, D., van Eck, J., Bouwmeester, R., van Dijk, E., & Abeln, S. (2022). How sticky are our proteins? Quantifying hydrophobicity of the human proteome. Bioinformatics advances, 2(1), vbac002.

Capel, H., Feenstra, K. A., & Abeln, S. (2022). Multi-task learning to leverage partially annotated data for PPI interface prediction. Scientific reports, 12(1), 10487. 

Capel, H., Weiler, R., Dijkstra, M., Vleugels, R., Bloem, P., & Feenstra, K. A. (2022). ProteinGLUE multi-task benchmark suite for self-supervised protein modeling. Scientific reports, 12(1), 16047. 

Stringer, B., de Ferrante, H., Abeln, S., Heringa, J., Feenstra, K. A., & Haydarlou, R. (2022). PIPENN: protein interface prediction from sequence with an ensemble of neural nets. Bioinformatics (Oxford, England), 38(8), 2111–2118.

Mavrina, E., Kimble, L., Waury, K., Gogishvili, D., Gómez de San José, N., Das, S., Coppens, S., Fernandes Gomes, B., Mravinacová, S., Wojdała, A. L., Bolsewig, K., Bayoumy, S., Burtscher, F., Mohaupt, P., Willemse, E., Teunissen, C., & MIRIADE consortium (2022). Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE). Frontiers in neurology, 13, 890638. 

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.

Liu, T., Lan, G., Feenstra, K. A., Huang, Z., & Heringa, J. (2022). Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases. Scientific reports, 12(1), 18977.

Lan, G., Liu, T., Wang, X., Pan, X., & Huang, Z. (2022). A semantic web technology index. Scientific reports, 12(1), 3672. 

Mouhib, H., Higuchi, A., Abeln, S., Yura, K., & Feenstra, K. A. (2022). Impact of pathogenic mutations of the GLUT1 glucose transporter on solute carrier dynamics using ComDYN enhanced sampling. F1000Research, 8, 322. 

Hou, Q., Stringer, B., Waury, K., Capel, H., Haydarlou, R., Xue, F., Abeln, S., Heringa, J., & Feenstra, K. A. (2021). SeRenDIP-CE: sequence-based interface prediction for conformational epitopes. Bioinformatics (Oxford, England), 37(20), 3421–3427. 

van Gils, J. H. M., van Dijk, E., 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), e1007767.

Fernandes Neto, J. M., Nadal, E., Bosdriesz, E., Ooft, S. N., Farre, L., McLean, C., Klarenbeek, S., Jurgens, A., Hagen, H., Wang, L., Felip, E., Martinez-Marti, A., Vidal, A., Voest, E., Wessels, L. F. A., van Tellingen, O., Villanueva, A., & Bernards, R. (2020). Multiple low dose therapy as an effective strategy to treat EGFR inhibitor-resistant NSCLC tumours. Nature communications, 11(1), 3157.