Prof. Dr. Enkelejda Miho
Prof. Dr. Enkelejda Miho
Activities at the FHNW
- Professor of Digital Life Sciences
- Team Leader, aiHealthLab
- Group Leader at Swiss Bioinformatics Institute
Teaching
Master of Science in Medical Informatics:
- Digital Transformation in Healthcare
- Artificial Intelligence in Drug Discovery
- Innovation Trends in Medical Informatics
Master of Science in Life Sciences, specialization in Data Science:
- Deep Learning
Research
Her research at the School of Life Sciences FHNW focuses on the use of computer science and artificial intelligence for drug discovery and personalized medicine. In doing so, she has a bridge function that is intended to link different life sciences processes with computer science.
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No peer reviewed content available
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Peer reviewedKruta, J., Carapito, R., Trendelenburg, M., Martin, T., Rizzi, M., Voll, R. E., Cavalli, A., Natali, E., Meier, P., Stawiski, M., Mosbacher, J., Mollet, A., Santoro, A., Capri, M., Giampieri, E., Schkommodau, E., & Miho, E. (2024). Machine learning for precision diagnostics of autoimmunity. Scientific Reports, 14(1), 27848. https://doi.org/10.1038/s41598-024-76093-7
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Peer reviewedSchaffer, A.-M., Fiala, G. J., Hils, M., Natali, E., Babrak, L., Herr, L. A., Romero-Mulero, M. C., Cabezas-Wallscheid, N., Rizzi, M., Miho, E., Schamel, W. W. A., & Minguet, S. (2024). Kidins220 regulates the development of B cells bearing the λ light chain. eLife. https://doi.org/10.7554/elife.83943
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Peer reviewedNatali, E. N., Horst, A., Meier, P., Greiff, V., Nuvolone, M., Babrak, L. M., Fink, K., & Miho, E. (2024). The dengue-specific immune response and antibody identification with machine learning. Npj Vaccines, 9(16). https://doi.org/10.1038/s41541-023-00788-7
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Peer reviewedNatali, E. N., Horst, A., Meier, P., Greiff, V., Nuvolone, M., Babrak, L. M., Fink, K., & Miho, E. (2024). Author Correction. The dengue-specific immune response and antibody identification with machine learning. Npj Vaccines, 9(16). https://doi.org/10.1038/s41541-024-00820-4
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Peer reviewedRobert, P. A., Akbar, R., Frank, R., Pavlović, M., Widrich, M., Snapkov, I., Slabodkin, A., Chernigovskaya, M., Scheffer, L., Smorodina, E., Rawat, P., Mehta, B. B., Vu, M. H., Mathisen, I. F., Prósz, A., Abram, K., Olar, A., Miho, E., Haug, D. T. T., et al. (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science, 2, 845–865. https://doi.org/10.1038/s43588-022-00372-4
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Peer reviewedRobert, P. A., Akbar, R., Pavlović, M., Widrich, M., Snapkov, I., Slabodkin, A., Chernigovskaya, M., Scheffer, L., Smorodina, E., Rawat, P., Mehta, B. B., Vu, M. H., Mathisen, I. F., Prósz, A., Abram, K., Olar, A., Miho, E., Haug, D. T. T., Lund-Johansen, F., et al. (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science, 2(12), 845–865. https://doi.org/10.1038/s43588-022-00372-4
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Peer reviewedDegen, M., Babrak, L., Smakaj, E., Agac, T., Asprion, P., Grimberg, F., Van der Werf, D., Van Ginkel, E. W., Tosoni, D. D., Clay, I., Brodbeck, D., Natali, E., Schkommodau, E., & Miho, E. (2022). RWD-Cockpit. Application for quality assessment of real-world data. JMIR Formative Research, 6(10). https://doi.org/10.2196/29920
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Peer reviewedCascino, P., Nevone, A., Piscitelli, M., Scopelliti, C., Girelli, M., Mazzini, G., Caminito, S., Russo, G., Milani, P., Basset, M., Foli, A., Fazio, F., Casarini, S., Massa, M., Bozzola, M., Ripepi, J., Sesta, M. A., Acquafredda, G., De Cicco, M., et al. (2022). Sequencing of the M protein. Toward personalized medicine in monoclonal gammopathies. American Journal of Hematology, 97(11). https://doi.org/10.1002/ajh.26684
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Peer reviewedCascino, P., Nevone, A., Piscitelli, M., Scopelliti, C., Girelli, M., Mazzini, G., Caminito, S., Russo, G., Milani, P., Basset, M., Foli, A., Fazio, F., Casarini, S., Massa, M., Bozzola, M., Ripepi, J., Sesta, M. A., Acquafredda, G., De Cicco, M., et al. (2022). Single-molecule real-time sequencing of the M protein.Toward personalized medicine in monoclonal gammopathies. American Journal of Hematology, 97(11), E389–E392. https://doi.org/10.1002/ajh.26684
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Peer reviewedBabrak, L., Marquez, S., Busse, C., Lees, W., Miho, E., Ohlin, M., Rosenfeld, A., Stervbo, U., Watson, C., & Schramm, C. (2022). Adaptive immune receptor repertoire (AIRR) community guide to TR and IG gene annotation. In A. W. Langerak & Department of Immunology Erasmus MC Rotterdam (Eds.), Immunogenetics Methods and Protocols (pp. 279–296). Springer. https://doi.org/10.1007/978-1-0716-2115-8_16
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Peer reviewedAkbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., Mehta, B. B., Miho, E., Lund-Johansen, F., Andersen, J. T., Hochreiter, S., Hobæk Haff, I., Klambauer, G., Sandve, G. K., & Greiff, V. (2022). In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs, 14(1). https://doi.org/10.1080/19420862.2022.2031482
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Peer reviewedBabrak, L., Smakaj, E., Agac, T., Asprion, P., Grimberg, F., Van der Werf, D., van Ginkel, E. W., Tosoni, D. D., Clay, I., Degen, M., Brodbeck, D., Natali, E. N., Schkommodau, E., & Miho, E. (2022). RWD-Cockpit: application for quality assessment of real-world data. JMIR Formative Research, 6(10). https://doi.org/10.2196/29920
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Peer reviewedHorst, A., Smakaj, E., Natali, E., Tosoni, D. D., Babrak, L., Meier, P., & Miho, E. (2021). Machine learning detects anti-DENV signatures in antibody repertoire sequences. Frontiers in Artificial Intelligence, 4. https://doi.org/10.3389/frai.2021.715462
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Peer reviewedNatali, E., Babrak, L., & Miho, E. (2021). Prospective artificial intelligence to dissect the dengue immune response and discover therapeutics. frontiers in immunology. https://doi.org/10.3389/fimmu.2021.574411
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Peer reviewedMessner, C., Babrak, L., Titolo, G., Caj, M., Miho, E., & Suter-Dick, L. (2021). Single Cell Gene Expression analysis in a 3D microtissue liver model reveals cell type-specific responses to pro-fibrotic TGF-β1 stimulation. International Journal of Molecular Sciences, 22(9). https://doi.org/10.3390/ijms22094372
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Peer reviewedMiho, E., Akbar, R., Pavlovic, M., Snapkov, I., Slabodkin, A., Scheffer, L., Haff, I. H., Tryslew Haug, D. T., Lund-Johanson, F., Safonova, Y., Greiff, V., Robert, P., Jeliazkov, J., Weber, C., & Sandve, G. (2021). A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell Reports, 34(11), 1–21. https://doi.org/10.1016/j.celrep.2021.108856
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Peer reviewedGrimberg, F., Asprion, P., Schneider, B., Miho, E., Babrak, L., & Habbabeh, A. (2021). The real-world data challenges radar: a review on the challenges and risks regarding the use of real-world data. Digital Biomarkers, 5(2), 148–157. https://doi.org/10.1159/000516178
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Peer reviewedGhraichy, M., Galson, J. D., Kovaltsuk, A., von Niederhäusern, V., Pachlopnik Schmid, J., Recher, M., Jauch, A. J., Miho, E., Kelly, D. F., Deane, C. M., & Trück, J. (2020). Maturation of the human immunoglobulin heavy chain repertoire with age. Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.01734
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Peer reviewedSmakaj, E., Babrak, L., Tosoni, D. D., Galli, C., & Miho, E. (2019). Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences. Bioinformatics. https://doi.org/10.1093/bioinformatics/btz845
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Ghraichy, M., Galson, J. D., Kovaltsuk, A., Niederhäusern, V. v., Schmid, J. P., Recher, M., Jauch, A. J., Miho, E., Kelly, D. F., Deane, C. M., & Trück, J. (2019). Maturation of the human B-cell receptor repertoire with age. bioRxiv. https://doi.org/10.1101/609651
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Peer reviewedMiho, E., Roškar, R., Greiff, V., & Reddy, S. T. (2019). Large-scale network analysis reveals the sequence space architecture of antibody repertoires. Nature Communications, 10, 1321. https://doi.org/10.1038/s41467-019-09278-8
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Peer reviewedMiho, E. (2019). Large-scale network analysis reveals the sequence space architecture of antibody repertoires. Nature Communications, 1–11. https://doi.org/10.1038/s41467-019-09278-8
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Peer reviewedBrown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019). Augmenting adaptive immunity. Progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires. Molecular Systems Design & Engineering, 4, 701–736. https://doi.org/10.1039/c9me00071b
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Grimberg, F., Asprion, P., Schneider, B., Miho, E., Babrak, L., & Habbabeh, A. (2019). Real World Data - Technologies, Research Questions and Applications - Study in Cooperation - School of Business & School of Life Science. Fachhochschule Nordwestschweiz FHNW. https://irf.fhnw.ch/handle/11654/43279
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Peer reviewedBabrak, L., & Miho, E. (2019). Traditional and Digital Biomarkers: Two Worlds Apart? Digital Biomarkers, 3, 92–102. https://doi.org/10.1159/000502000
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Peer reviewedFriedensohn, S., Lindner, J. M., Cornacchione, V., Iazeolla, M., Miho, E., Zingg, A., Meng, S., Traggiai, E., & Reddy, S. T. (2018). Synthetic standards combined with error and bias correction improve the accuracy and quantitative resolution of antibody repertoire sequencing in human naïve and memory B cells. Frontiers in Immunology, 9. https://doi.org/10.3389/fimmu.2018.01401
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Peer reviewedFriedensohn, S., Lindner, J. M., Cornacchione, V., Iazeolla, M., Miho, E., Zingg, A., Meng, S., Traggiai, E., & Reddy, S. T. (2018). Synthetic standards combined with error and bias correction improve the accuracy and quantitative resolution of antibody repertoire sequencing in human naïve and memory B cells. Frontiers in Immunology, 9, 1401. https://doi.org/10.3389/fimmu.2018.01401
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Peer reviewedMiho, E., Yermanos, A., Weber, C. R., Berger, C. T., Reddy, S. T., & Greiff, V. (2018). Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires. Frontiers in Immunology, 9. https://doi.org/10.3389/fimmu.2018.00224
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No peer reviewed content available
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Natali, E. N., Horst, A., Meier, P., Greiff, V., Nuvolone, M., Babrak, L. M., Djordjevic, K., Fink, K., Traggiai, E., & Miho, E. (2022, September 21). Computational deconvolution of the dengue immune response complexity with identification of novel broadly neutralizing antibodies. Postdoc Network Meeting. https://doi.org/10.26041/fhnw-9932
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Miho, E. (2019, September 17). Transitioning from Traditional Computational Modeling to Machine Learning and AI. 17th Annual Discovery on Target. https://irf.fhnw.ch/handle/11654/30660
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Miho, E. (2019, January 28). Prediction of personal antibody repertoires. Applied Machine Learning Days (AMLD 2019). https://irf.fhnw.ch/handle/11654/30655
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Miho, E. (2019, January 17). Network Modeling to Predict Personal Immune Scenarios. PEPTALK, Protein Science Week. https://irf.fhnw.ch/handle/11654/30659
Contact
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Prof. Dr. Enkelejda Miho
- Professor of Digital Life Sciences
- Telephone
- +41 61 228 58 47 (direct)
- ZW5rZWxlamRhLm1paG9AZmhudy5jaA==
- School of Life Sciences FHNW
Institute for Medical Engineering and Medical Informatics
Hofackerstrasse 30
4132 Muttenz
Institute for Medical Engineering and Medical Informatics
FHNW University of Applied Sciences and Arts Northwestern Switzerland
School of Life Sciences,
Institute for Medical Engineering and Medical Informatics
Hofackerstrasse 30
CH - 4132 Muttenz
- Telephone
- +41 61 228 54 19
- ZXJpay5zY2hrb21tb2RhdUBmaG53LmNo