Prof. Dr. Enkelejda Miho
Prof. Dr. Enkelejda Miho
Tätigkeiten an der FHNW
- Professorin für Digital Life Sciences
- Arbeitsgruppenleiterin, aiHealthLab
- Gruppenleiterin am Swiss Bioinformatics Institute
Lehre
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
Forschung
Ihre Forschung an der Hochschule für Life Sciences FHNW konzentriert sich auf den Einsatz von Informatik und künstlicher Intelligenz für die Wirkstoffforschung und personalisierte Medizin. Sie hat dabei eine Brückenfunktion inne, die unterschiedliche Life Sciences-Prozesse mit der Informatik verknüpfen soll.
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Keine peer-reviewed Inhalte verfügbar
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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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Keine peer-reviewed Inhalte verfügbar
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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
Kontakt
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Prof. Dr. Enkelejda Miho
- Professorin für Digital Life Sciences
- Telefonnummer
- +41 61 228 58 47 (Direkt)
- ZW5rZWxlamRhLm1paG9AZmhudy5jaA==
- Hochschule für Life Sciences FHNW
Institut für Medizintechnik und Medizininformatik
Hofackerstrasse 30
4132 Muttenz
Institut für Medizintechnik und Medizininformatik
Fachhochschule Nordwestschweiz FHNW
Hochschule für Life Sciences
Institut für Medizintechnik und Medizininformatik
Hofackerstrasse 30
4132 Muttenz
- Telefon
- +41 61 228 54 19
- ZXJpay5zY2hrb21tb2RhdUBmaG53LmNo