A selection of our most recent publications.

2025

Stohn, T., van Eijl, R., Mulder, K. W., Wessels, L. F. A., Bosdriesz, E. (2025). Reconstructing and comparing signal transduction networks from single cell protein quantification data. bioRxiv. https://doi.org/10.1101/2024.03.29.587331

Krämer, N., van Eijl, R., Stohn, T., Tanis, S., Wessels, L., Bosdriesz, E., Mulder, K. W. (2025). Cell-state specific drug-responses are associated with differences in signaling network wiring. bioRxiv. https://doi.org/10.1101/2025.01.27.635060

Lakbir, S., de Wit, R., de Bruijn, I., Kundra, R., Madupuri, R., Gao, J., Schultz, N., Meijer, G.A., Heringa, J., Fijneman, R.J.A., Abeln, S. (2025). Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers. npj Precision Oncology, 9(140). https://doi.org/10.1038/s41698-025-00922-9

Paesani, M., Goetzee, A. G., Abeln, S., & Mouhib, H. (2025). Odorant binding proteins facilitate the gas-phase uptake of odorants through the nasal mucus. Chemistry (Weinheim an Der Bergstrasse, Germany), 31(2), e202403058. https://doi.org/10.1002/chem.202403058

Arend, L., Adamowicz, K., Schmidt, J. R., Burankova, Y., Zolotareva, O., Tsoy, O., Pauling, J. K., Kalkhof, S., Baumbach, J., List, M., & Laske, T. (2025). Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE. Briefings in Bioinformatics, 26(3). https://doi.org/10.1093/bib/bbaf201

Thomas, D. P. G., Garcia Fernandez, C. M., Haydarlou, R., & Feenstra, K. A. (2025). PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology. Scientific Reports, 15(1), 4391. https://doi.org/10.1038/s41598-025-88445-y

2024

Jeuken, G. S., Käll L. (2024) Pathway analysis through mutual information. Bioinformatics Volume, 40(1), btad776. https://doi.org/10.1093/bioinformatics/btad776

Lakbir, S., Buranelli, C., Meijer, G. A., Heringa, J., Fijneman, R. J. A., & Abeln, S. (2024). CIBRA identifies genomic alterations with a system-wide impact on tumor biology. Bioinformatics (Oxford, England), 40(Suppl 2), ii37–ii44. https://doi.org/10.1093/bioinformatics/btae384

Liu, T., Feenstra, K. A., Huang, Z., & Heringa, J. (2024). Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph. Bioinformatics (Oxford, England), 40(1). https://doi.org/10.1093/bioinformatics/btad771

Rubio-Alarcón, C., Stelloo, E., Vessies, D. C. L., van ’t Erve, I., Mekkes, N. J., Swennenhuis, J., Lakbir, S., van Bree, E. J., Tijssen, M., Delis-van Diemen, P., Lanfermeijer, M., Linders, T., van den Broek, D., Punt, C. J. A., Heringa, J., Meijer, G. A., Abeln, S., Feitsma, H., & Fijneman, R. J. A. (2024). High prevalence of chromosomal rearrangements and LINE retrotranspositions detected in formalin-fixed, paraffin-embedded colorectal cancer tissue. The Journal of Molecular Diagnostics: JMD, 26(12), 1065–1080. https://doi.org/10.1016/j.jmoldx.2024.08.004

2023

Gavai, A., Bouzembrak, Y., Mu, W., Martin, F., Kaliyaperumal, R., van Soest, J., Choudhury, A., Heringa, J., Dekker, A., & Marvin, H. J. P. (2023). Applying federated learning to combat food fraud in food supply chains. Npj Science of Food, 7(1), 46. Sutikdja, L. W., Nguyen, H. V. L., Jelisavac, D., Stahl, W., & Mouhib, H. (2023). Benchmarking quantum chemical methods for accurate gas-phase structure predictions of carbonyl compounds: the case of ethyl butyrate. Physical Chemistry Chemical Physics: PCCP, 25(11), 7688–7696. https://doi.org/10.1039/d2cp05774c

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. https://doi.org/10.1038/s41598-023-33045-x

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. https://doi.org/10.1186/s13195-023-01200-1 

2022

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. https://doi.org/10.1016/j.isci.2022.104760

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. https://doi.org/10.1016/j.ejca.2022.09.034 

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. https://doi.org/10.1186/s40364-022-00425-w 

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). https://doi.org/10.1371/journal.pcbi.1010669 

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. https://doi.org/10.1093/bioadv/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. https://doi.org/10.1038/s41598-022-13951-2 

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. https://doi.org/10.1038/s41598-022-19608-4 

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. https://doi.org/10.1093/bioinformatics/btac071

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. https://doi.org/10.3389/fneur.2022.890638 

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. https://doi.org/10.1145/3486897

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. https://doi.org/10.1038/s41598-022-21735-x

Lan, G., Liu, T., Wang, X., Pan, X., & Huang, Z. (2022). A semantic web technology index. Scientific reports, 12(1), 3672. https://doi.org/10.1038/s41598-022-07615-4 

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. https://doi.org/10.12688/f1000research.18553.2 

2021

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. https://doi.org/10.1093/bioinformatics/btab321 

2020

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. https://doi.org/10.1371/journal.pcbi.1007767

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. https://doi.org/10.1038/s41467-020-16952-9