Prof. Dr. David B. Blumenthal
Biomedical Network Science Lab / Department Artificial Intelligence in Biomedical Engineering (AIBE)
The Biomedical Network Science (BIONETS) lab investigates molecular disease mechanisms using techniques from network science, combinatorial optimization, and artificial intelligence. We develop algorithms and tools to mine multi-omics data for such mechanisms and to individuate novel strategies for mechanistically grounded drug repurposing and causally effective treatments of complex diseases. We also develop privacy-preserving decentralized biomedical AI solutions, which enable cross-institutional studies on sensitive data.
Research projects
- Design and development of disease module mining algorithms.
- Federated biomedical artificial intelligence.
- Systems and network medicine.
- Simulation, modelling, and detection of genetic epistasis.
- Development of methods and tools for in silico drug repurposing.
-
M3-SFB 1755/1 TP Z01 (SB/878): Integrative tissue profiling, omics data analysis support, and FAIR data management (Z01)
(Third Party Funds Group – Sub project)
Overall project: SFB 1755: CASCAID - Cellular and Systems Control of Autoimmune Disease
Project leader: , ,
Term: 1. April 2026 - 31. December 2029
Acronym: M3-SFB 1755/1 TP Z01 (SB/878)
Funding source: DFG / Sonderforschungsbereich (SFB) -
M3-SFB 1755/1 TP #04: Tissue-level determinants of macrophage polarisation and histological healing (TP04)
(Third Party Funds Group – Sub project)
Overall project: SFB 1755: CASCAID - Cellular and Systems Control of Autoimmune Disease
Project leader: ,
Term: 1. April 2026 - 31. December 2029
Acronym: M3-SFB 1755/1 TP #04
Funding source: DFG / Sonderforschungsbereich (SFB)Inflammation leaves lasting imprints beyond visible scarring. Conventional treatments suppress inflammation but often do not lead to complete tissue healing. Using advanced tissue imaging, gene expression analysis and machine learning methods, we aim to identify the key regulators of niche regeneration and create molecular signatures that distinguish fully healed from partially healed tissues. We want to redefine the term “healing” by investigating how cells, molecules and tissue structures interact with each other.
-
Prediction and detection of response rates to immune-checkpoint inhibition through spatial transcriptomics
(Third Party Funds Single)
Project leader:
Term: 1. March 2026 - 29. February 2028
Funding source: Deutsche Krebshilfe -
AML-MRD: BZKF Translationsgruppe - Determination of residual disease in AML using AI-supported analysis of flow cytometry data
(Third Party Funds Single)
Project leader: ,
Term: 1. January 2025 - 31. December 2026
Acronym: AML-MRD
Funding source: andere Förderorganisation -
FLabNet: Federated network medicine for laboratory data in paediatric oncology
(Third Party Funds Group – Overall project)
Project leader: ,
Term: 1. November 2024 - 31. October 2026
Acronym: FLabNet
Funding source: BMFTR / VerbundprojektIn FLabNet, we will harness the potential of algorithmic network biology and distributed machine learning to address two exemplary unmet needs in paediatric oncology: prediction ofchemotherapy side effects like neutropenic fever and early-stage detection of rare malignantdiseases such as myeloproliferative neoplasms. Based on >54 million laboratory test resultsfrom >500,000 patients from the Core Dataset of the German Medical Informatics Initiative (MII),we will create personalised networks, where nodes represent individual laboratory measurementsand edges encode patient-specific relationships. We hypothesise the emerging personal graph representations to capture the unique spectra and dependencies of the individual patients’ health anddisease characteristics. The networks will be used as signatures for label-efficient graph-based pre-dictors such as graph kernels; and we will provide privacy-preserving federated implementationsof our predictors that are fully interoperable with MII standards. To achieve its objectives, ourconsortium combines expertise in algorithmic systems biology (FAU), paediatric oncology (UKER),quantitative analysis of laboratory data (UKER), federated learning for biomedicine (Bitspark GmbH& FAU), and professional software development (Bitspark GmbH). These synergistic skill sets willenable us to combine laboratory diagnostics, computational systems medicine, and privacy-preserving machine learning, advancing the state of the art in quantitative analysis of laboratory data for precision medicine in paediatric oncology and beyond.
-
CoBiNet: High-resolution protein-protein interaction networks for biomedical research
(Third Party Funds Group – Overall project)
Project leader:
Term: 1. July 2024 - 30. June 2027
Acronym: CoBiNet
Funding source: andere Förderorganisation
URL: https://www.cobinet.ai/ -
DyHealthNet: A Platform for Dynamic Exploration of the Cooperative Health Research in South Tyrol Study Data via Multi-Level Network Medicine
(Third Party Funds Single)
Project leader:
Term: 1. December 2023 - 30. November 2026
Acronym: DyHealthNet
Funding source: Deutsche Forschungsgemeinschaft (DFG)
URL: https://www.dyhealthnet.ai/The Cooperative Health Research in South Tyrol (CHRIS) study offers a comprehensive overview of the health state of >13,000 adults in the middle and upper Val Venosta. It is the largest population-based molecular study in Italy with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with lifestyle and environment in the general population. In CHRIS, the combination of molecular profiling data, such as genomics and metabolomics, together with important baseline clinical and lifestyle data offers vast opportunities for understanding physiological changes that could lead to clinical complications or indicate the prevalence or early onset of diseases together with their molecular underpinnings.
Where disease-focused studies often have a clear hypothesis that dictates the necessary statistical analyses, population-based cohorts such as CHRIS are more versatile and allow both testing existing hypotheses as well as generating new hypotheses that arise from statistically significant associations of the available data. Ideally, this type of explorative analysis is open to biomedical researchers that do not necessarily have experience with data analysis or machine learning. Network-based approaches are ideally suited for studying heterogeneous biomedical data, giving rise to the field of network medicine. However, network medicine techniques have so far mainly been used in the context of studies focusing on individual diseases. Network-based platforms for the explorative analysis of population-based cohort data do not exist.
In DyHealthNet, we will close this gap and develop a network-based data analysis platform, which will allow to integrate heterogeneous data and support explorative data analytics over dynamically generated subsets of the CHRIS study data. To fully leverage the potential of the available multi-level data, the DyHealthNet platform combines (1) data integration using standardized medical information models (HL7 FHIR), (2) innovative index structures for scalable dynamic analysis, (3) machine learning, and (4) visual analytics. DyHealthNet will render the CHRIS population cohort data accessible for state-of-the-art privacy-preserving, network-based data analysis. DyHealthNet will hence enable mining of context-specific pathomechanisms for precision medicine, and will serve as a blueprint for dynamic explorative analysis of multi-level cohort data worldwide. To achieve these objectives, the DyHeathNet consortium combines expertise in population-based cohort studies (Fuchsberger) and in the development of complex algorithms for the analysis of molecular networks (Blumenthal), applied biomedical AI and software systems (List), and customized index structures for scalable data management (Gamper).
-
AI4MDD: AI-Powered Prognosis of Treatment Response in Major Depression Disorder
(Third Party Funds Single)
Project leader:
Term: 1. July 2023 - 30. September 2026
Funding source: Industrie
2026
- Ghirotto, B., Gonçalves, L.E., Ruder, V., James, C., Gerasimova, E., Rizo, T.,... Winner, B. (2026). TNF alpha unmasks enteric malate aspartate shuttle dysfunction bridging Parkinson disease and intestinal inflammation. Nature Communications, 17(1). https://doi.org/10.1038/s41467-026-71317-y
- Joeres, R., Blumenthal, D.B., & Kalinina, O.V. (2026). Addendum: Data splitting against information leakage with DataSAIL. Nature Communications, 17(1). https://doi.org/10.1038/s41467-025-67495-w
- Woller, F., Martini, P., Sen, S., Blumenthal, D.B., & Hartebrodt, A. (2026). SignifiKANTE: Efficient P-value computation for gene regulatory networks. Bioinformatics (Oxford, England). https://doi.org/10.1093/bioinformatics/btag531
2025
- Galindez, G., Lopez, B.A., Blumenthal, D.B., & Kacprowski, T. (2025). Identification of transcriptional regulators using a combined disease module identification and prize-collecting Steiner tree approach. Network and Systems Medicine, 1(1), 42-56. https://doi.org/10.14293/NSM.25.1.0003
- Gerasimova, E., Beenen, A., Kachkin, D., Regensburger, M., Zundler, S., Blumenthal, D.B.,... Prots, I. (2025). Novel co-culture model of T cells and midbrain organoids for investigating neurodegeneration in Parkinson’s disease. npj Parkinson’s Disease, 11(1). https://doi.org/10.1038/s41531-025-00882-8
- Joeres, R., Blumenthal, D.B., & Kalinina, O.V. (2025). Data splitting to avoid information leakage with DataSAIL. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-58606-8
- Lindacher, J., Hartebrodt, A., Dingfelder, J., Lukas, P., Würthner, L., Völkl, S.,... Lutzny-Geier, G. (2025). 3D Tumor microenvironment interaction reveals AP-1 complex regulation and contact-mediated reprogramming of bone marrow stromal cells in chronic lymphocytic leukemia. HemaSphere, 9(8). https://doi.org/10.1002/hem3.70199
- Martini, P., Hartebrodt, A., de Almeida, G.P., Hackstein, C.P., Zehn, D., & Blumenthal, D.B. (2025). SwitchTFI: identifying transcription factors driving cell differentiation. Genome Biology, 26(1). https://doi.org/10.1186/s13059-025-03876-0
- Meyerhöfer, N., Krogan, N.J., Polacco, B.J., & Blumenthal, D.B. (2025). Inference of differential kinase interaction networks with KINference. Bioinformatics, 41(7). https://doi.org/10.1093/bioinformatics/btaf349
- Müller, F., Schwingen, N.R., Hagen, M., Scholz, J.K., Aigner, M., Wirsching, A.,... Völkl, S. (2025). Comparison of the safety profiles of CD19-targeting CAR T-cell therapy in patients with SLE and B-cell lymphoma. Blood. https://doi.org/10.1182/blood.2025028375
- Reim, T., Hartebrodt, A., Blumenthal, D.B., Bernett, J., & List, M. (2025). Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65. Bioinformatics, 41, i590-i598. https://doi.org/10.1093/bioinformatics/btaf192
- Sarkar, S., & Blumenthal, D.B. (2025). Gene Co-expression Networks are Poor Proxies for Expert-Curated Gene Regulatory Networks. In Luc Brun, Vincenzo Carletti, Sébastien Bougleux, Benoît Gaüzère (Eds.), Graph-Based Representations in Pattern Recognition. GbRPR 2025. (pp. 37-46). Cham: Springer Science and Business Media Deutschland GmbH.
- Schnitzerlein, M., Greto, E., Wegner, A., Möller, A., Aust, O., Ben Brahim, O.,... Uderhardt, S. (2025). Cellular morphodynamics as quantifiers for functional states of resident tissue macrophages in vivo. PLoS Computational Biology, 21(5). https://doi.org/10.1371/journal.pcbi.1011859
- Wallnig, J., Brun, L., Gaüzère, B., Bougleux, S., Yger, F., & Blumenthal, D.B. (2025). A Differentiable Approximation of the Graph Edit Distance. In Andrea Torsello, Luca Rossi, Luca Cosmo, Giorgia Minello (Eds.), Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, S+SSPR 2024 (pp. 1-10). Cham: Springer Science and Business Media Deutschland GmbH.
- Woller, F., Arend, L., Fuchsberger, C., List, M., & Blumenthal, D.B. (2025). NApy: efficient statistics in Python for large-scale heterogeneous data with enhanced support for missing data. GigaScience, 14. https://doi.org/10.1093/gigascience/giaf140
2024
- Bernett, J., Blumenthal, D.B., Grimm, D.G., Haselbeck, F., Joeres, R., Kalinina, O.V., & List, M. (2024). Guiding questions to avoid data leakage in biological machine learning applications. Nature Methods, 21(8), 1444-1453. https://doi.org/10.1038/s41592-024-02362-y
- Bernett, J., Blumenthal, D.B., & List, M. (2024). Cracking the black box of deep sequence-based protein-protein interaction prediction. Briefings in Bioinformatics, 25(2). https://doi.org/10.1093/bib/bbae076
- Blumenthal, D.B., Lucchetta, M., Kleist, L., Fekete, S.P., List, M., & Schaefer, M.H. (2024). Emergence of power law distributions in protein-protein interaction networks through study bias. eLife, 13. https://doi.org/10.7554/eLife.99951
- Galindez, G., List, M., Baumbach, J., Völker, U., Mäder, U., Blumenthal, D.B., & Kacprowski, T. (2024). Inference of differential gene regulatory networks using boosted differential trees. Bioinformatics Advances, 4(1). https://doi.org/10.1093/bioadv/vbae034
- Hartebrodt, A., Röttger, R., & Blumenthal, D.B. (2024). Federated singular value decomposition for high-dimensional data. Data Mining and Knowledge Discovery, 38, 938 - 975. https://doi.org/10.1007/s10618-023-00983-z
- Hoffmann, M., Poschenrieder, J.M., Incudini, M., Baier, S., Fritz, A., Maier, A.,... Blumenthal, D.B. (2024). Network medicine-based epistasis detection in complex diseases: Ready for quantum computing. Nucleic Acids Research, 52(17), 10144-10160. https://doi.org/10.1093/nar/gkae697
- Kersting, J., Lazareva, O., Louadi, Z., Baumbach, J., Blumenthal, D.B., & List, M. (2024). DysRegNet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics. British Journal of Pharmacology. https://doi.org/10.1111/bph.17395
- Khnaisser, C., Hamrouni, H., Blumenthal, D.B., Dignös, A., & Gamper, J. (2024). Efficiently Labeling and Retrieving Temporal Anomalies in Relational Databases. Information Systems Frontiers. https://doi.org/10.1007/s10796-024-10495-w
- Maier, A., Hartung, M., Abovsky, M., Adamowicz, K., Bader, G.D., Baier, S.,... Baumbach, J. (2024). Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing. Nucleic Acids Research, 52(W1), W481-W488. https://doi.org/10.1093/nar/gkae388
- Maier, A., Rafiei, M., Anastasi, E., Zolotareva, O., Skelton, J., Elkjaer, M.L.,... Baumbach, J. (2024). NeDRex-Web: An Interactive Web Tool for Drug Repurposing by Exploring Heterogeneous Molecular Networks. In Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 (pp. 429-434). Institute of Electrical and Electronics Engineers Inc..
- Menche, C., Schuhwerk, H., Armstark, I., Gupta, P., Fuchs, K., van Roey, R.,... Stemmler, M.P. (2024). ZEB1-mediated fibroblast polarization controls inflammation and sensitivity to immunotherapy in colorectal cancer. EMBO Reports. https://doi.org/10.1038/s44319-024-00186-7
- Sarkar, S., Möller, A., Hartebrodt, A., Erdmann, M., Ostalecki, C., Baur, A., & Blumenthal, D.B. (2024). Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma. npj Systems Biology and Applications, 10(1). https://doi.org/10.1038/s41540-024-00474-x
- Wang, D.D., Katoch, M., Jabari, S., Blümcke, I., Blumenthal, D.B., Lu, D.H.,... Piao, Y.S. (2024). Correction to: The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D (Acta Neuropathologica Communications, (2023), 11, 1, (129), 10.1186/s40478-023-01618-6). Acta Neuropathologica Communications, 12(1). https://doi.org/10.1186/s40478-024-01752-9
2023
- Boria, N., Kiederle, J., Yger, F., & Blumenthal, D.B. (2023). The edge-preservation similarity for comparing rooted, unordered, node-labeled trees. Pattern Recognition Letters, 167, 189-195. https://doi.org/10.1016/j.patrec.2023.02.017
- Gerlach, L., & Blumenthal, D.B. (2023). On the role of network topology in German-Jewish recommendation letter networks in the early twentieth century. Applied Network Science, 8(1). https://doi.org/10.1007/s41109-023-00550-x
- Hoffmann, M., Trummer, N., Schwartz, L., Jankowski, J., Lee, H.K., Willruth, L.-L.,... List, M. (2023). TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors. GigaScience, 12. https://doi.org/10.1093/gigascience/giad026
- Ketteler, A., & Blumenthal, D.B. (2023). Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer. Briefings in Bioinformatics, 24(6). https://doi.org/10.1093/bib/bbad413
- Sadegh, S., Skelton, J., Anastasi, E., Maier, A., Adamowicz, K., Möller, A.,... Blumenthal, D.B. (2023). Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-37349-4
- Sarkar, S., Lucchetta, M., Maier, A., Abdrabbou, M.M.M., Baumbach, J., List, M.,... Blumenthal, D.B. (2023). Online bias-aware disease module mining with ROBUST-Web. Bioinformatics, 35(6). https://doi.org/10.1093/bioinformatics/btad345
- Wang, D.-D., Katoch, M., Jabari, S., Blümcke, I., Blumenthal, D.B., Lu, D.-H.,... Piao, Y.-S. (2023). The specific DNA methylation landscape in focal cortical dysplasia ILAE type 3D. Acta Neuropathologica Communications, 11(1). https://doi.org/10.1186/s40478-023-01618-6
2022
- Adamowicz, K., Maier, A., Baumbach, J., & Blumenthal, D.B. (2022). Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbac247
- Bernett, J., Krupke, D., Sadegh, S., Baumbach, J., Fekete, S.P., Kacprowski, T.,... Blumenthal, D.B. (2022). Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics, 38(6), 1600-1606. https://doi.org/10.1093/bioinformatics/btab876
- Blumenthal, D.B., Bougleux, S., Dignoes, A., & Gamper, J. (2022). Enumerating dissimilar minimum cost perfect and error-correcting bipartite matchings for robust data matching. Information Sciences, 596, 202-221. https://doi.org/10.1016/j.ins.2022.03.017
- Khnaisser, C., Hamrouni, H., Blumenthal, D.B., Dignoes, A., & Gamper, J. (2022). Querying Temporal Anomalies in Healthcare Information Systems and Beyond. In Chiusano S, Cerquitelli T, Wrembel R (Eds.), Advances in Databases and Information Systems (ADBIS 2022) (pp. 209--222). Turin, IT: Cham: Springer International Publishing.
- Torkzadehmahani, R., Nasirigerdeh, R., Blumenthal, D.B., Kacprowski, T., List, M., Matschinske, J.,... Baumbach, J. (2022). Privacy-Preserving Artificial Intelligence Techniques in Biomedicine. Methods of Information in Medicine. https://doi.org/10.1055/s-0041-1740630
2021
- Bause, F., Blumenthal, D.B., Schubert, E., & Kriege, N.M. (2021). Metric Indexing for Graph Similarity Search. In Reyes N, Connor R, Kriege N, Kazempour D, Bartolini I, Schubert E, Chen J (Eds.), Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021) (pp. 323--336). Dortmund, DE: Cham: Springer International Publishing.
- Blumenthal, D.B., Boria, N., Bougleux, S., Brun, L., Gamper, J., & Gaüzère, B. (2021). Scalable generalized median graph estimation and its manifold use in bioinformatics, clustering, classification, and indexing. Information Systems, 100. https://doi.org/10.1016/j.is.2021.101766
- Blumenthal, D.B., Gamper, J., Bougleux, S., & Brun, L. (2021). Upper Bounding Graph Edit Distance Based on Rings and Machine Learning. International Journal of Pattern Recognition and Artificial Intelligence. https://doi.org/10.1142/S0218001421510083
- Gnecco, L., Boria, N., Bougleux, S., Yger, F., & Blumenthal, D.B. (2021). The Minimum Edit Arborescence Problem and Its Use in Compressing Graph Collections. In Reyes N, Connor R, Kriege N, Kazempour D, Bartolini I, Schubert E, Chen J (Eds.), Proceedings of the 14th International Conference on Similarity Search and Applications (SISAP 2021) (pp. 337--351). Dortmund, DE: Cham: Springer International Publishing.
- Hartebrodt, A., Nasirigerdeh, R., Blumenthal, D.B., & Röttger, R. (2021). Federated Principal Component Analysis for Genome-Wide Association Studies. In 21st IEEE International Conference on Data Mining (ICDM) (pp. 1090-1095). Auckland, New Zealand: IEEE.
- Lazareva, O., Baumbach, J., List, M., & Blumenthal, D.B. (2021). On the limits of active module identification. Briefings in Bioinformatics, 22(5). https://doi.org/10.1093/bib/bbab066
- Matschinske, J., Benis, A., Alcaraz, N., Golebiewski, M., Grimm, D.G., Heumos, L.,... Blumenthal, D.B. (2021). The AIMe registry for artificial intelligence in biomedical research. Nature Methods, 18, 1128 - 1131. https://doi.org/10.1038/s41592-021-01241-0
- Nasirigerdeh, R., Torkzadehmahani, R., Baumbach, J., & Blumenthal, D.B. (2021). On the Privacy of Federated Pipelines. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21) (pp. 1975 - 1979). Virtual Event, CA: New York, NY, USA: ACM.
- Sadegh, S., Skelton, J., Anastasi, E., Bernett, J., Blumenthal, D.B., Galindez, G.,... Kacprowski, T. (2021). Network medicine for disease module identification and drug repurposing with the NeDRex platform. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-27138-2
- Zolotareva, O., Nasirigerdeh, R., Matschinske, J., Torkzadehmahani, R., Bakhtiari, M., Frisch, T.,... Baumbach, J. (2021). Flimma: a federated and privacy-aware tool for differential gene expression analysis. Genome Biology, 22(1). https://doi.org/10.1186/s13059-021-02553-2
2020
- Blumenthal, D.B., Baumbach, J., Hoffmann, M., Kacprowski, T., & List, M. (2020). A framework for modeling epistatic interaction. Bioinformatics, 37(12), 1708 - 1716. https://doi.org/10.1093/bioinformatics/btaa990
- Blumenthal, D.B., Boria, N., Gamper, J., Bougleux, S., & Brun, L. (2020). Comparing heuristics for graph edit distance computation. Vldb Journal, 29(1), 419-458. https://doi.org/10.1007/s00778-019-00544-1
- Blumenthal, D.B., & Gamper, J. (2020). On the exact computation of the graph edit distance. Pattern Recognition Letters, 134, 46-57. https://doi.org/10.1016/j.patrec.2018.05.002
- Blumenthal, D.B., Viola, L., List, M., Baumbach, J., Tieri, P., & Kacprowski, T. (2020). EpiGEN: An epistasis simulation pipeline. Bioinformatics, 36(19), 4957-4959. https://doi.org/10.1093/bioinformatics/btaa245
- Boria, N., Blumenthal, D.B., Bougleux, S., & Brun, L. (2020). Improved local search for graph edit distance. Pattern Recognition Letters, 129, 19-25. https://doi.org/10.1016/j.patrec.2019.10.028
- Bougleux, S., Gauzere, B., Blumenthal, D.B., & Brun, L. (2020). Fast linear sum assignment with error-correction and no cost constraints. Pattern Recognition Letters, 134, 37-45. https://doi.org/10.1016/j.patrec.2018.03.032
- Chondrogiannis, T., Bouros, P., Gamper, J., Leser, U., & Blumenthal, D.B. (2020). Finding k-shortest paths with limited overlap. Vldb Journal. https://doi.org/10.1007/s00778-020-00604-x
- Helmer, S., Blumenthal, D.B., & Paschen, K. (2020). What is meaningful research and how should we measure it? Scientometrics, 125(1), 153-169. https://doi.org/10.1007/s11192-020-03649-5
- Lazareva, O., Canzar, S., Yuan, K., Baumbach, J., Blumenthal, D.B., Tieri, P.,... List, M. (2020). BiCoN: Network-constrained biclustering of patients and omics data. Bioinformatics, 37(16), 2398 - 2404. https://doi.org/10.1093/bioinformatics/btaa1076
- Matschinske, J., Salgado-Albarran, M., Sadegh, S., Bongiovanni, D., Baumbach, J., & Blumenthal, D.B. (2020). Individuating Possibly Repurposable Drugs and Drug Targets for COVID-19 Treatment through Hypothesis-Driven Systems Medicine Using CoVex. Assay and Drug Development Technologies, 18(8), 348-355. https://doi.org/10.1089/adt.2020.1010
- Sadegh, S., Matschinske, J., Blumenthal, D.B., Galindez, G., Kacprowski, T., List, M.,... Baumbach, J. (2020). Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17189-2