Milena Pavlovic

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

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• Widrich, Michael; Schäfl, Bernhard; Pavlović, Milena; Ramsauer, Hubert; Gruber, Lukas & Holzleitner, Markus [Vis alle 11 forfattere av denne artikkelen] (2020). Modern Hopfield Networks and Attention for Immune Repertoire Classification. Vis sammendrag

  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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