Miho, Enkelejda

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Miho
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Enkelejda
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Miho, Enkelejda

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2019 - 201972020 - 20222
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Gerade angezeigt 1 - 9 von 9
  • Vorschaubild nicht verfügbar
    Publikation
    Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction
    (Nature, 19.12.2022) Robert, Philippe A.; Akbar, Rahmad; Pavlović, Milena; Widrich, Michael; Snapkov, Igor; Slabodkin, Andrei; Chernigovskaya, Maria; Scheffer, Lonneke; Smorodina, Eva; Rawat, Puneet; Mehta, Brij Bhushan; Vu, Mai Ha; Mathisen, Ingvild Frøberg; Prósz, Aurél; Abram, Krzysztof; Olar, Axel; Miho, Enkelejda; Haug, Dag Trygve Tryslew; Lund-Johansen, Fridtjof; Hochreiter, Sepp; Hobæk Haff, Ingrid; Klambauer, Günter; Sandve, Geir Kjetil; Greiff, Victor [in: Nature Computational Science]
    Machine learning (ML) is a key technology for accurate prediction of antibody–antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody–antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Sequencing of the M protein. Toward personalized medicine in monoclonal gammopathies
    (Wiley, 23.08.2022) Cascino, Pasquale; Nevone, Alice; Piscitelli, Maggie; Scopelliti, Claudia; Girelli, Maria; Mazzini, Giulia; Caminito, Serena; Russo, Giancarlo; Milani, Paolo; Basset, Marco; Foli, Andrea; Fazio, Francesca; Casarini, Simona; Massa, Margherita; Bozzola, Margherita; Ripepi, Jessica; Sesta, Melania Antonietta; Acquafredda, Gloria; De Cicco, Marica; Moretta, Antonia; Rognoni, Paola; Milan, Enrico; Ricagno, Stefano; Lavatelli, Francesca; Petrucci, Maria Teresa; Klersy, Catherine; Merlini, Giampaolo; Palladini, Giovanni; Nuvolone, Mario; Miho, Enkelejda [in: American Journal of Hematology]
    Each patient with a monoclonal gammopathy has a unique monoclonal (M) protein, whose sequence can be used as a tumoral fingerprint to track the presence of the B cell or plasma cell (PC) clone itself. Moreover, the M protein can directly cause potentially life-threatening organ damage, which is dictated by the specific, patient's unique clonal light and/or heavy chain amino acid sequence, as in patients affected by immunoglobulin light chain (AL) amyloidosis.1 However, patients' specific M protein sequences remain mostly undefined and molecular mechanisms underlying M protein-related clinical manifestations are largely obscure.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences
    (Oxford University Press, 24.12.2019) Smakaj, Erand; Babrak, Lmar; Tosoni, Deniz David; Galli, Christa; Miho, Enkelejda [in: Bioinformatics]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    06 - Präsentation
  • Vorschaubild nicht verfügbar
    Publikation
    Large-scale network analysis reveals the sequence space architecture of antibody repertoires
    (Nature, 21.03.2019) Miho, Enkelejda [in: Nature Communications]
    The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. The major principles that define the architecture of antibody repertoires have remained largely unknown. Here, we establish a high-performance computing platform to construct large-scale networks from comprehensive human and murine antibody repertoire sequencing datasets (>100,000 unique sequences). Leveraging a network-based statistical framework, we identify three fundamental principles of antibody repertoire architecture: reproducibility, robustness and redundancy. Antibody repertoire networks are highly reproducible across individuals despite high antibody sequence dissimilarity. The architecture of antibody repertoires is robust to the removal of up to 50–90% of randomly selected clones, but fragile to the removal of public clones shared among individuals. Finally, repertoire architecture is intrinsically redundant. Our analysis provides guidelines for the large-scale network analysis of immune repertoires and may be used in the future to define disease-associated and synthetic repertoires.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Prediction of personal antibody repertoires
    (28.01.2019) Miho, Enkelejda
    06 - Präsentation
  • Vorschaubild nicht verfügbar
    Publikation
    Network Modeling to Predict Personal Immune Scenarios
    (17.01.2019) Miho, Enkelejda
    06 - Präsentation
  • Vorschaubild nicht verfügbar
    Publikation
    Traditional and Digital Biomarkers: Two Worlds Apart?
    (Karger, 2019) Babrak, Lmar; Miho, Enkelejda [in: Digital Biomarkers]
    The identification and application of biomarkers in the clinical and medical fields has an enor - mous impact on society. The increase of digital devices and the rise in popularity of health- related mobile apps has produced a new trove of biomarkers in large, diverse, and complex data. However, the unclear definition of digital biomarkers, population groups, and their in - tersection with traditional biomarkers hinders their discovery and validation. We have identi - fied current issues in the field of digital biomarkers and put forth suggestions to address them during the DayOne Workshop with participants from academia and industry. We have found similarities and differences between traditional and digital biomarkers in order to synchronize semantics, define unique features, review current regulatory procedures, and describe novel applications that enable precision medicine.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Real World Data - Technologies, Research Questions and Applications - Study in Cooperation - School of Business & School of Life Science
    (Fachhochschule Nordwestschweiz FHNW, 2019) Grimberg, Frank; Asprion, Petra; Schneider, Bettina; Miho, Enkelejda; Babrak, Lmar; Habbabeh, Ali
    In this research report of the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), a classification of ‘Real World Data’ into the research landscape takes place. In addition, an identification of the still open research questions is done based on the fundamental principles and properties. The manifold potential of this relatively new data set is illustrated by a presentation of the already existing but also conceivable future application possibilities. Finally, the contribution of the FHNW, based on its specific competencies, to the further application of the dataset is shown.
    05 - Forschungs- oder Arbeitsbericht