Milena Pavlovic

• Kanduri, Chakravarthi; Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Chernigovskaya, Maria & Greiff, Victor [Show all 7 contributors for this article] (2022). Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification. GigaScience. ISSN 2047-217X. 11(05). doi: 10.1093/gigascience/giac046. Full text in Research Archive
• Akbar, Rahmad; Robert, Philippe Paul Auguste; Weber, Cédric R.; Widrich, Michael; Frank, Robert & Pavlovic, Milena [Show all 19 contributors for this article] (2022). In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs. ISSN 1942-0862. 14:e2031482(1), p. 1–18. doi: 10.1080/19420862.2022.2031482.
• Slabodkin, Andrei; Chernigovskaia, Maria; Mikocziova, Ivana; Akbar, Rahmad; Scheffer, Lonneke & Pavlović, Milena [Show all 16 contributors for this article] (2021). Individualized VDJ recombination predisposes the available Ig sequence space. Genome Research. ISSN 1088-9051. p. 2209–2224. doi: 10.1101/gr.275373.121. Full text in Research Archive
• Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Kanduri, Chakravarthi; Kompova, Radmila & Vazov, Nikolay Aleksandrov [Show all 41 contributors for this article] (2021). The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence. 3(11), p. 936–944. doi: 10.1038/s42256-021-00413-z.
• Arnaout, Ramy A.; Luning Prak, Eline T.; Schwab, Nicholas; Rubelt, Florian; Greiff, Victor & Pavlović, Milena [Show all 32 contributors for this article] (2021). The Future of Blood Testing Is the Immunome. Frontiers in Immunology. ISSN 1664-3224. 12:626793, p. 1–6. doi: 10.3389/fimmu.2021.626793. Full text in Research Archive
• Akbar, Rahmad; Robert, Philippe Paul Auguste; Pavlović, Milena; Jeliazkov, Jeliazko R.; Snapkov, Igor & Slabodkin, Andrei [Show all 15 contributors for this article] (2021). A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell reports. ISSN 2211-1247. 34:108856(11), p. 1–21. doi: 10.1016/j.celrep.2021.108856. Full text in Research Archive
• Yao, Ying; Zia, Asima; Neumann, Ralf Stefan; Pavlović, Milena; Balaban, Gabriel & Lundin, Knut Erik Aslaksen [Show all 8 contributors for this article] (2021). T cell receptor repertoire as a potential diagnostic marker for celiac disease. Clinical Immunology. ISSN 1521-6616. 222:108621, p. 1–7. doi: 10.1016/j.clim.2020.108621. Full text in Research Archive
• Weber, Cédric R.; Akbar, Rahmad; Yermanos, Alexander; Pavlovic, Milena; Snapkov, Igor & Sandve, Geir Kjetil [Show all 8 contributors for this article] (2020). immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking. Bioinformatics. ISSN 1367-4803. 36(11), p. 3594–3596. doi: 10.1093/bioinformatics/btaa158. Full text in Research Archive
• Brown, Alex; Snapkov, Igor; Akbar, Rahmad; Pavlovic, Milena; Miho, Enkelejda & Sandve, Geir Kjetil [Show all 7 contributors for this article] (2019). Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires. Molecular Systems Design & Engineering. ISSN 2058-9689. 4(4), p. 701–736. doi: 10.1039/c9me00071b.

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• Widrich, Michael; Schäfl, Bernhard; Pavlović, Milena; Ramsauer, Hubert; Gruber, Lukas & Holzleitner, Markus [Show all 11 contributors for this article] (2020). Modern Hopfield Networks and Attention for Immune Repertoire Classification. Show summary

  A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns. We exploit this high storage capacity of modern Hopfield networks to solve a challenging multiple instance learning (MIL) problem in computational biology: immune repertoire classification. In immune repertoire classification, a vast number of immune receptors are used to predict the immune status of an individual. This constitutes a MIL problem with an unprecedentedly massive number of instances, two orders of magnitude larger than currently considered problems, and with an extremely low witness rate. Accurate and interpretable machine learning methods solving this problem could pave the way towards new vaccines and therapies, which is currently a very relevant research topic intensified by the COVID-19 crisis. In this work, we present our 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. We demonstrate that DeepRC outperforms all other methods with respect to predictive performance on large-scale experiments including simulated and real-world virus infection data and enables the extraction of sequence motifs that are connected to a given disease class. Source code and datasets: https://github.com/ml-jku/DeepRC.

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