Milena Pavlović
University of Oslo
21 Papers
32 Citations
Milena Pavlović is an academic researcher from University of Oslo. The author has contributed to research in topics: Computer science & Immune receptor. The author has an hindex of 10, co-authored 21 publications.
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Papers
A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.
Rahmad Akbar,Philippe Robert,Milena Pavlović,Jeliazko R. Jeliazkov,Igor Snapkov,Andrei Slabodkin,Cédric R. Weber,Lonneke Scheffer,Enkelejda Miho,Ingrid Hobæk Haff,Dag Haug,Fridtjof Lund-Johansen,Yana Safonova,Geir Kjetil Sandve,Victor Greiff +14 more
TL;DR: In this paper, the authors identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions, and show that this vocabulary enables the machine learnability of antibody-antigen binding using generative machine learning.
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Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires
Alex J. Brown,Igor Snapkov,Rahmad Akbar,Milena Pavlović,Enkelejda Miho,Geir Kjetil Sandve,Victor Greiff +6 more
- 05 Aug 2019
TL;DR: The adaptive immune system is a natural diagnostic sensor and therapeutic that can be used as a diagnostic and therapeutic tool in the medical field.
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•Posted Content
Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich,Bernhard Schäfl,Hubert Ramsauer,Milena Pavlović,Lukas Gruber,Markus Holzleitner,Johannes Brandstetter,Geir Kjetil Sandve,Victor Greiff,Sepp Hochreiter,Günter Klambauer +10 more
TL;DR: This work presents a novel method DeepRC that integrates transformer-like attention, or equivalently modern Hopfield networks, into deep learning architectures for massive MIL such as immune repertoire classification, and demonstrates that DeepRC outperforms all other methods with respect to predictive performance on large-scale experiments.
One billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction
Philippe Robert,Rahmad Akbar,Frank R,Milena Pavlović,Michael Widrich,Igor Snapkov,Maria Chernigovskaya,Lonneke Scheffer,Andrei Slabodkin,Brij Bhushan Mehta,Vu Mh,Prósz A,Abram K,Abram K,Olar A,Enkelejda Miho,Haug Dtt,Fridtjof Lund-Johansen,Sepp Hochreiter,Ingrid Hobæk Haff,Günter Klambauer,Geir Kjetil Sandve,Greiff +22 more
TL;DR: The Absolut! as mentioned in this paper software suite enables the generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity.
Individualized VDJ recombination predisposes the available Ig sequence space.
Andrei Slabodkin,Maria Chernigovskaya,Ivana Mikocziova,Rahmad Akbar,Lonneke Scheffer,Milena Pavlović,Habib Bashour,Igor Snapkov,Brij Bhushan Mehta,Cédric R. Weber,José F. Gutierrez-Marcos,Ludvig M. Sollid,Ingrid Hobæk Haff,Geir Kjetil Sandve,Philippe Robert,Victor Greiff +15 more
TL;DR: In this paper, a sensitivity-tested distance measure was devised to enable inter-individual comparison of VDJ recombination models, and it was shown that population-wide individualized recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences.
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