Author

E. Miho

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Immunology· 6

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Research Focus:cell type annotation· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Assay:protein structure· 2 Research Focus:protein structure prediction· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

A. Slabodkin· 3 shared F. Lund-Johansen· 3 shared G. K. Sandve· 3 shared I. Snapkov· 3 shared L. Scheffer· 3 shared M. Pavlovic· 3 shared P. A. Robert· 3 shared R. Akbar· 3 shared

Activity by Year

2018-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersImmunology

A. Slabodkin

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Author profile →

6 papers

Recent·Most cited

Kidins220 regulates the development of B cells bearing the {lambda} light chain

A.-M. Schaffer, G. J. Fiala, M. Hils, E. Natali, L. Babrak, L. A. Herr, M. Rizzi, E. Miho, W. Schamel, S. Minguet

This study shows Kidins220 regulates λLC B cell development by supporting precursor survival and optimizing pre-BCR/BCR signaling for λLC locus opening and recombination.

bioRxiv · Apr 4, 2022Immunology

In silico proof of principle of machine learning-based antibody design at unconstrained scale

R. Akbar, P. A. Robert, C. R. Weber, M. Widrich, R. Frank, M. Pavlovic, L. Scheffer, M. Chernigovskaya, I. Snapkov, A. Slabodkin, B. B. Mehta, E. Miho, F. Lund-Johansen, J. T. Andersen, S. Hochreiter, , V. Greiff

This paper demonstrates that deep generative models can design high-affinity, developable antibodies at scale using synthetic data and transfer learning.

bioRxiv · Jul 8, 2021Immunology

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

P. A. Robert, R. Akbar, R. Frank, M. Pavlovic, M. Widrich, I. Snapkov, A. Slabodkin, M. Chernigovskaya, L. Scheffer, E. Smorodina, P. Rawat, B. B. Mehta, M. H. Vu, I. Froberg Mathisen, A. Prosz, , V. Greiff

The paper introduces Absolut!, a software suite generating synthetic antibody-antigen structures to benchmark machine learning methods for antibody specificity prediction.

bioRxiv · Jul 6, 2021Immunology

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

R. Akbar, P. A. Robert, M. Pavlovic, J. R. Jeliazkov, I. Snapkov, A. Slabodkin, C. R. Weber, L. Scheffer, E. Miho, I. H. Haff, D. T. Tryslew Haug, F. Lund-Johansen, Y. Safonova, G. K. Sandve, V. Greiff

The paper identifies a compact vocabulary of paratope-epitope interaction motifs that enables machine learning prediction of antibody-antigen binding.

bioRxiv · Mar 4, 2020Immunology

Maturation of the human B-cell receptor repertoire with age

M. Ghraichy, J. D. Galson, A. Kovaltsuk, V. von Niederhaeusern, J. Pachlopnik Schmid, M. Recher, A. Jauch, E. Miho, D. F. Kelly, C. M. Deane, J. Trueck

This study examines age-related changes in human B-cell receptor repertoires, finding significant shifts in immunoglobulin gene usage and self-reactive antibody frequencies during early life and aging.

bioRxiv · Dec 20, 2019 · PreprintImmunology

Synthetic standards combined with error and bias correction improves the accuracy and quantitative resolution of antibody repertoire sequencing in human naive and memory B cells

S. Friedensohn, J. M. Lindner, V. Cornacchione, M. Iazeolla, E. Miho, A. Zingg, S. Meng, E. Traggiai, S. T. Reddy

bioRxiv · Mar 21, 2018Immunology