Victor Greiff

• Iacobescu, Maria; Pop, Cristina; Uifălean, Alina; Mogoşan, Cristina; Cenariu, Diana & Zdrenghea, Mihnea [Show all 13 contributors for this article] (2024). Unlocking protein-based biomarker potential for graft-versus-host disease following allogenic hematopoietic stem cell transplants. Frontiers in Immunology. ISSN 1664-3224. 15. doi: 10.3389/fimmu.2024.1327035.
• Sørensen, Christoffer V.; Hofmann, Nils; Rawat, Puneet; Sørensen, Frederik V.; Ljungars, Anne & Greiff, Victor [Show all 8 contributors for this article] (2024). ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns. Bioinformatics Advances. ISSN 2635-0041. 4(1). doi: 10.1093/bioadv/vbae020.
• Balashova, Daria; van Schaik, Barbera D C; Stratigopoulou, Maria; Guikema, Jeroen E J; Caniels, Tom G. & Claireaux, Mathieu [Show all 12 contributors for this article] (2024). Systematic evaluation of B-cell clonal family inference approaches. BMC Immunology. ISSN 1471-2172. 25(1). doi: 10.1186/s12865-024-00600-8.
• Mhanna, Vanessa; Bashour, Habib; Lê Quý, Khang; Barennes, Pierre; Rawat, Puneet & Greiff, Victor [Show all 7 contributors for this article] (2024). Adaptive immune receptor repertoire analysis. Nature Reviews Disease Primers. ISSN 2056-676X. 4(1). doi: 10.1038/s43586-023-00284-1.
• Pavlović, Milena; al Hajj, Ghadi; Kanduri, Chakravarthi; Pensar, Johan; Wood, Mollie Elizabeth & Sollid, Ludvig Magne [Show all 8 contributors for this article] (2024). Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nature Machine Intelligence. 6(1), p. 15–24. doi: 10.1038/s42256-023-00781-8.
• ULVMOEN, AINA; Greiff, Victor; Bechensteen, Anne Grete & Inngjerdingen, Marit (2024). NKG2A discriminates natural killer cells with a suppressed phenotype in pediatric acute leukemia. Journal of Leukocyte Biology. ISSN 0741-5400. 115(2), p. 334–343. doi: 10.1093/jleuko/qiad112.
• Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario & Babrak, Lmar Marie [Show all 8 contributors for this article] (2024). The dengue-specific immune response and antibody identification with machine learning. npj Vaccines. ISSN 2059-0105. 9(1). doi: 10.1038/s41541-023-00788-7.
• Khan, Asif; Cowen-Rivers, Alexander I.; Grosnit, Antoine; Deik, Derrick-Goh-Xin; Robert, Philippe A. & Greiff, Victor [Show all 15 contributors for this article] (2023). Toward real-world automated antibody design with combinatorial Bayesian optimization. Cell Reports Methods. 3:100374(1), p. 1–21. doi: 10.1016/j.crmeth.2022.100374. Full text in Research Archive
• Sharma, Divya; Rawat, Puneet; Greiff, Victor; Janakiraman, Vani & Gromiha, M. Michael (2023). Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochimica et Biophysica Acta - Molecular Basis of Disease. ISSN 0925-4439. 1870(2). doi: 10.1016/j.bbadis.2023.166959.
• Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke & Gainullin, Murat [Show all 13 contributors for this article] (2023). Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Frontiers in Immunology. ISSN 1664-3224. 14:1265044, p. 1–14. doi: 10.3389/fimmu.2023.1265044. Full text in Research Archive
• Elster, Christin; Ommer-Bläsius, Miriam; Lang, Alexander; Vajen, Tanja; Pfeiler, Susanne & Feige, Milena [Show all 16 contributors for this article] (2023). Application and challenges of TCR and BCR sequencing to investigate T- and B-cell clonality in elastase-induced experimental murine abdominal aortic aneurysm. Frontiers in Cardiovascular Medicine. ISSN 2297-055X. 10:1221620, p. 1–15. doi: 10.3389/fcvm.2023.1221620. Full text in Research Archive
• Kanduri, Chakravarthi; Scheffer, Lonneke; Pavlović, Milena; Rand, Knut Dagestad; Chernigovskaia, Maria & Pirvandy, Oz [Show all 9 contributors for this article] (2023). simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. GigaScience. ISSN 2047-217X. 12, p. 1–16. doi: 10.1093/gigascience/giad074. Full text in Research Archive
• Erasmus, M. Frank; Ferrara, Fortunato; D’Angelo, Sara; Spector, Laura; Leal-Lopes, Camila & Teixeira, André A. [Show all 17 contributors for this article] (2023). Insights into next generation sequencing guided antibody selection strategies. Scientific Reports. ISSN 2045-2322. 13:18370(1), p. 1–16. doi: 10.1038/s41598-023-45538-w. Full text in Research Archive
• Heimli, Marte; Flåm, Siri Tennebø; Hjorthaug, Hanne Sagsveen; Bjørnstad, Pål Marius; Chernigovskaia, Maria & Le, Quy Khang [Show all 9 contributors for this article] (2023). Human thymic putative CD8αα precursors exhibit a biased TCR repertoire in single cell AIRR-seq. Scientific Reports. ISSN 2045-2322. 13:17714(1), p. 1–13. doi: 10.1038/s41598-023-44693-4. Full text in Research Archive
• Tigu, Adrian Bogdan; Constantinescu, Catalin Sorin; Teodorescu, Patric; Kegyes, David; Munteanu, Raluca & Feder, Richard [Show all 23 contributors for this article] (2023). Design and preclinical testing of an anti-CD41 CAR T cell for the treatment of acute megakaryoblastic leukaemia. Journal of Cellular and Molecular Medicine. ISSN 1582-1838. 27(19), p. 2864–2875. doi: 10.1111/jcmm.17810. Full text in Research Archive
• Timis, Tanase; Bergthorsson, Jon Thor; Greiff, Victor; Cenariu, Mihai & Cenariu, Diana (2023). Pathology and Molecular Biology of Melanoma. Current Issues in Molecular Biology. ISSN 1467-3037. 45(7), p. 5575–5597. doi: 10.3390/cimb45070352. Full text in Research Archive
• Rappazzo, C. Garrett; Fernandez-Quintero, Monica L.; Mayer, Andreas; Wu, Nicholas C.; Greiff, Victor & Guthmiller, Jenna J. (2023). Defining and studying B cell receptor and TCR interactions. Journal of Immunology. ISSN 0022-1767. 211(3), p. 311–322. doi: 10.4049/jimmunol.2300136.
• Tigu, Adrian Bogdan; Hotea, Ionut; Drula, Rares; Zimta, Alina-Andreea; Dirzu, Noemi & Santa, Maria [Show all 15 contributors for this article] (2023). RNA sequencing suggests that non-coding RNAs play a role in the development of acquired haemophilia. Journal of Cellular and Molecular Medicine. ISSN 1582-1838. 27(13), p. 1790–1796. doi: 10.1111/jcmm.17741. Full text in Research Archive
• Cenariu, Diana; Rus, Ioana; Bergthorsson, Jon Thor; Grewal, Ravnit; Cenariu, Mihai & Greiff, Victor [Show all 14 contributors for this article] (2023). Flow Cytometry of CD5-Positive Hairy Cell Leukemia. Molecular Diagnosis & Therapy. ISSN 1177-1062. 27(5), p. 593–599. doi: 10.1007/s40291-023-00658-x.
• Constantinescu, Cătălin; Constantinescu, Roxana; Bergthorsson, Jon Thor; Greiff, Victor; Tanase, Alina & Colita, Anca [Show all 8 contributors for this article] (2023). Pitfalls in patenting academic CAR-T cells therapy. Expert Opinion on Therapeutic Patents. ISSN 1354-3776. 33(5), p. 339–348. doi: 10.1080/13543776.2023.2220883.
• Høye, Eirik; Dagenborg, Vegar Johansen; Kristensen, Annette Torgunrud; Lund-Andersen, Christin; Fretland, Åsmund Avdem & Lorenz, Susanne [Show all 13 contributors for this article] (2023). T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases. GigaScience. ISSN 2047-217X. 12. doi: 10.1093/gigascience/giad032. Full text in Research Archive
• Fernández-Quintero, Monica L.; Ljungars, Anne; Waibl, Franz; Greiff, Victor; Andersen, Jan Terje & Gjølberg, Torleif [Show all 16 contributors for this article] (2023). Assessing developability early in the discovery process for novel biologics. mAbs. ISSN 1942-0862. 15:2171248(1), p. 1–19. doi: 10.1080/19420862.2023.2171248. Full text in Research Archive
• Han, Jiami; Masserey, Solène; Shlesinger, Danielle; Kuhn, Raphael; Papadopoulou, Chrysa & Agrafiotis, Andreas [Show all 14 contributors for this article] (2022). Echidna: Integrated simulations of single-cell immune receptor repertoires and transcriptomes. Bioinformatics Advances. ISSN 2635-0041. 2(1), p. 1–10. doi: 10.1093/bioadv/vbac062. Full text in Research Archive
• Robert, Philippe Paul Auguste; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael & Snapkov, Igor [Show all 25 contributors for this article] (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science. ISSN 2662-8457. 2(12), p. 845–865. doi: 10.1038/s43588-022-00372-4.
• Robert, Philippe Paul Auguste & Greiff, Victor (2022). Bridging the neutralization gap for unseen antibodies. Nature Machine Intelligence. 5, p. 8–10. doi: 10.1038/s42256-022-00594-1.
• Pennell, Matt; Rodriguez, Oscar L.; Watson, Corey T. & Greiff, Victor (2022). The evolutionary and functional significance of germline immunoglobulin gene variation. Trends in immunology. ISSN 1471-4906. 44(1), p. 7–21. doi: 10.1016/j.it.2022.11.001. Full text in Research Archive
• Dorraji, Seyed Esmaeil; Borgen, Elin; Segura-Pena, Dario; Rawat, Puneet; Smorodina, Eva & Dunn, Claire [Show all 10 contributors for this article] (2022). Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform. Cancers. ISSN 2072-6694. 14:4859(19), p. 1–24. doi: 10.3390/cancers14194859. Full text in Research Archive Show summary

  Simple Summary In the present study, we addressed the unmet need for a molecular antibody (mAb) with high affinity and specificity against a truncated hyperactive isoform of human epidermal growth factor receptor 2 (HER2), called 611-carboxy terminal fragment (CTF)-p95HER2. Patients with p95HER2+ breast cancer are at risk of developing metastatic breast cancer with a poor prognosis and resistance to therapies targeting full-length HER2. We have generated a mAb named Oslo-2, which react specifically with 611-CTF-p95HER2 and has a high affinity. We also characterized the antigenic determinant (epitope) on the p95HER2 protein and the antigen-binding site (paratope) on the Oslo-2 mAb. The antibody can be used to develop antibody- or cell-based therapies targeting p95HER2, as well as a diagnostic assay to identify p95HER2+ disease. Abstract The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays. Keywords: HER2; p95HER2; antibody; tumor-specific; breast cancer; cancer stem cells; HDX-MS; epitope and paratope mapping; docking

• Weber, Cédric R.; Rubio, Teresa; Wang, Longlong; Zhang, Wei; Robert, Philippe Paul Auguste & Akbar, Rahmad [Show all 15 contributors for this article] (2022). Reference-based comparison of adaptive immune receptor repertoires. Cell Reports Methods. 2(8). doi: 10.1016/j.crmeth.2022.100269. Full text in Research Archive
• Perruzza, Lisa; Strati, Francesco; Raneri, Matteo; Li, Hai; Gargari, Giorgio & Rezzonico-Jost, Tanja [Show all 17 contributors for this article] (2022). Apyrase-mediated amplification of secretory IgA promotes intestinal homeostasis. Cell reports. ISSN 2211-1247. 40:111112(3), p. 1–25. doi: 10.1016/j.celrep.2022.111112. Full text in Research Archive
• Wilman, Wiktoria; Wróbel, Sonia; Bielska, Weronika; Deszynski, Piotr; Dudzic, Paweł & Jaszczyszyn, Igor [Show all 13 contributors for this article] (2022). Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Briefings in Bioinformatics. ISSN 1467-5463. 23:bbac267(4), p. 1–20. doi: 10.1093/bib/bbac267. Full text in Research Archive
• Rognes, Torbjørn; Scheffer, Lonneke; Greiff, Victor & Sandve, Geir Kjetil (2022). CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching. Bioinformatics. ISSN 1367-4803. 38(17), p. 4230–4232. doi: 10.1093/bioinformatics/btac505. Full text in Research Archive
• Akbar, Rahmad; Bashour, Habib; Rawat, Puneet; Robert, Philippe Paul Auguste; Smorodina, Eva & Cotet, Tudor-Stefan [Show all 15 contributors for this article] (2022). Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs. ISSN 1942-0862. 14:e2008790(1), p. 1–35. doi: 10.1080/19420862.2021.2008790. Full text in Research Archive
• Greiff, Victor (2022). A Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data. Immunoinformatics. ISSN 2667-1190. doi: 10.1016/j.immuno.2022.100012.
• Kanduri, Chakravarthi; Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Chernigovskaia, 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. Full text in Research Archive
• Grevys, Algirdas; Frick, Rahel; Mester, Simone; Flem-Karlsen, Karine; Nilsen, Jeannette & Foss, Stian [Show all 13 contributors for this article] (2022). Antibody variable sequences have a pronounced effect on cellular transport and plasma half-life. iScience. ISSN 2589-0042. 25:103746(2), p. 1–22. doi: 10.1016/j.isci.2022.103746. Full text in Research Archive
• Dahal-Koirala, Shiva; Balaban, Gabriel; Neumann, Ralf Stefan; Scheffer, Lonneke; Lundin, Knut & Greiff, Victor [Show all 9 contributors for this article] (2022). TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. Briefings in Bioinformatics. ISSN 1467-5463. 23(2), p. 1–14. doi: 10.1093/bib/bbab566. Full text in Research Archive Show summary

  T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5′ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.

• Mathew, Nimitha R.; Kalathilparambil Jayanthan, Jayalal; Smirnov, Ilya V.; Robinson, Jonathan L.; Axelsson, Hannes & Nakka, Sravya S. [Show all 20 contributors for this article] (2021). Single-cell BCR and transcriptome analysis after influenza infection reveals spatiotemporal dynamics of antigen-specific B cells. Cell reports. ISSN 2211-1247. 35:109286(12), p. 1–25. doi: 10.1016/j.celrep.2021.109286. Full text in Research Archive
• Mikocziova, Ivana; Peres, Ayelet; Gidoni, Moriah; Greiff, Victor; Yaari, Gur & Sollid, Ludvig Magne (2021). Germline polymorphisms and alternative splicing of human immunoglobulin light chain genes. iScience. ISSN 2589-0042. 24:103192(10), p. 1–19. doi: 10.1016/j.isci.2021.103192. Full text in Research Archive
• Mikocziova, Ivana; Greiff, Victor & Sollid, Ludvig Magne (2021). Immunoglobulin germline gene variation and its impact on human disease. Genes and Immunity. ISSN 1466-4879. 22(4), p. 205–217. doi: 10.1038/s41435-021-00145-5. Full text in Research Archive
• Amoriello, Roberta; Chernigovskaia, Mariia; Greiff, Victor; Carnasciali, Alberto; Massacesi, Luca & Barilaro, Alessandro [Show all 13 contributors for this article] (2021). TCR repertoire diversity in Multiple Sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood. EBioMedicine. ISSN 2352-3964. 68:103429, p. 1–13. doi: 10.1016/j.ebiom.2021.103429. Full text in Research Archive
• Robert, Philippe Paul Auguste; Kunze-Schumacher, Heike; Greiff, Victor & Krueger, Andreas (2021). Modeling the dynamics of T-cell development in the thymus. Entropy. ISSN 1099-4300. 23:437(4). doi: 10.3390/e23040437. Full text in Research Archive
• 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. 31(12), p. 2209–2225. 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.
• Neumeier, Daniel; Pedrioli, Alessandro; Genovese, Alessandro; Sandu, Ioana; Ehling, Roy & Hong, Kai-Lin [Show all 19 contributors for this article] (2021). Profiling the specificity of clonally expanded plasma cells during chronic viral infection by single-cell analysis. European Journal of Immunology. ISSN 0014-2980. 52(2), p. 297–311. doi: 10.1002/eji.202149331. Full text in Research Archive
• Snapkov, Igor; Chernigovskaia, Maria; Sinitcyn, Pavel; Le, Quy Khang; Nyman, Tuula Anneli & Greiff, Victor (2021). Progress and challenges in mass spectrometry-based analysis of antibody repertoires. Trends in Biotechnology. ISSN 0167-7799. 00, p. 1–19. doi: 10.1016/j.tibtech.2021.08.006. Full text in Research Archive
• Mester, Simone; Evers, Mitchell; Meyer, Saskia; Nilsen, Jeannette; Greiff, Victor & Sandlie, Inger [Show all 8 contributors for this article] (2021). Extended plasma half-life of albumin-binding domain fused human IgA upon pH-dependent albumin engagement of human FcRn in vitro and in vivo. mAbs. ISSN 1942-0862. 13:1893888(1), p. 1–14. doi: 10.1080/19420862.2021.1893888. Full text in Research Archive
• Øjlert, Åsa Kristina; Nebdal, Daniel J.H.; Snapkov, Igor; Olsen, Vibeke; Kidman, Joel & Greiff, Victor [Show all 8 contributors for this article] (2021). Dynamic changes in the T cell receptor repertoire during treatment with radiotherapy combined with an immune checkpoint inhibitor. Molecular Oncology. ISSN 1574-7891. p. 1–11. doi: 10.1002/1878-0261.13082. Full text in Research Archive
• 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
• Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification . bioRxiv. ISSN 2692-8205.
• Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires. bioRxiv. ISSN 2692-8205.
• Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). One billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction . bioRxiv. ISSN 2692-8205. doi: 10.1101/2021.07.06.451258.
• 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
• Laustsen, Andreas H.; Greiff, Victor; Karatt-Vellatt, Aneesh; Muyldermans, Serge & Jenkins, Timothy P. (2021). Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery. Trends in Biotechnology. ISSN 0167-7799. 39(12), p. 1263–1273. doi: 10.1016/j.tibtech.2021.03.003. Full text in Research Archive
• Riedel, René; Addo, Richard; Ferreira-Gomes, Marta; Heinz, Gitta Anne; Heinrich, Frederik & Kummer, Jannis [Show all 33 contributors for this article] (2020). Discrete populations of isotype-switched memory B lymphocytes are maintained in murine spleen and bone marrow. Nature Communications. ISSN 2041-1723. 11(1). doi: 10.1038/s41467-020-16464-6.
• Fallet, Bénédict; Hao, Yi; Florova, Marianna; Cornille, Karen; de los Aires, Alba Verge & Girelli Zubani, Giulia [Show all 15 contributors for this article] (2020). Chronic viral infection promotes efficient germinal center B cell responses. Cell reports. ISSN 2211-1247. 30(4), p. 1013–1026. doi: 10.1016/j.celrep.2019.12.023. Full text in Research Archive
• Kräutler, Nike Julia; Yermanos, Alexander; Pedrioli, Alessandro; Welten, Suzanne P.M.; Lorgé, Dominique & Greczmiel, Ute [Show all 16 contributors for this article] (2020). Quantitative and Qualitative Analysis of Humoral Immunity Reveals Continued and Personalized Evolution in Chronic Viral Infection. Cell reports. ISSN 2211-1247. 30(4), p. 997–1012. doi: 10.1016/j.celrep.2019.12.088. Full text in Research Archive
• Yermanos, Alexander; Kräutler, Nike Julia; Pedrioli, Alessandro; Menzel, Ulrike; Greiff, Victor & Stadler, Tanja [Show all 8 contributors for this article] (2020). IgM Antibody Repertoire Fingerprints in Mice Are Personalized but Robust to Viral Infection Status. Frontiers in Cellular and Infection Microbiology. ISSN 2235-2988. 10:254, p. 1–13. doi: 10.3389/fcimb.2020.00254. Full text in Research Archive
• Yermanos, Alexander; Dounas, Andreas; Greiff, Victor; Stadler, Tanja; Oxenius, Annette & Reddy, Sai T. (2020). Inter- and intraspecies comparison of phylogenetic fingerprints and sequence diversity of immunoglobulin variable genes. Immunogenetics. ISSN 0093-7711. 72(5), p. 279–294. doi: 10.1007/s00251-020-01164-8. Full text in Research Archive
• Amoriello, Roberta; Greiff, Victor; Aldinucci, Alessandra; Bonechi, Elena; Carnasciali, Alberto & Peruzzi, Benedetta [Show all 12 contributors for this article] (2020). The TCR Repertoire Reconstitution in Multiple Sclerosis: Comparing One-Shot and Continuous Immunosuppressive Therapies. Frontiers in Immunology. ISSN 1664-3224. 11:559, p. 1–19. doi: 10.3389/fimmu.2020.00559. Full text in Research Archive
• Li, Hai; Limenitakis, Julien P.; Greiff, Victor; Yilmaz, Bahtiyar; Schären, Olivier & Urbaniak, Camilla [Show all 15 contributors for this article] (2020). Mucosal or systemic microbiota exposures shape the B cell repertoire. Nature. ISSN 0028-0836. 584(7820), p. 274–278. doi: 10.1038/s41586-020-2564-6. Full text in Research Archive
• Greiff, Victor; Yaari, Gur & Cowell, Lindsay G. (2020). Mining adaptive immune receptor repertoires for biological and clinical information using machine learning. Current Opinion in Systems Biology. ISSN 2452-3100. 24, p. 109–119. doi: 10.1016/j.coisb.2020.10.010.

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• Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke & Gainullin, Murat [Show all 13 contributors for this article] (2024). Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence (Frontiers in Immunology, (2023), 14, (1265044), 10.3389/fimmu.2023.1265044). Frontiers in Immunology. ISSN 1664-3224. 15. doi: 10.3389/fimmu.2024.1377041.
• Le, Quy Khang; Chernigovskaia, Mariia; Greiff, Victor & Nyman, Tuula Anneli (2024). Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.11.01.565093.
• Natali, Eriberto Noel; Horst, Alexander; Meier, Patrick; Greiff, Victor; Nuvolone, Mario & Babrak, Lmar Marie [Show all 8 contributors for this article] (2024). Correction to: The dengue-specific immune response and antibody identification with machine learning (npj Vaccines, (2024), 9, 1, (16), 10.1038/s41541-023-00788-7). npj Vaccines. ISSN 2059-0105. 9(1). doi: 10.1038/s41541-024-00820-4.
• Mhanna, Vanessa; Bashour, Habib; Lê Quý, Khang; Barennes, Pierre; Rawat, Puneet & Greiff, Victor [Show all 7 contributors for this article] (2024). Correction to: Adaptive immune receptor repertoire analysis (Nature Reviews Methods Primers, (2024), 4, 1, (6), 10.1038/s43586-023-00284-1). Nature reviews methods primers. ISSN 2662-8449. doi: 10.1038/s43586-024-00299-2.
• Erasmus, M. Frank; Ferrara, Fortunato; D’Angelo, Sara; Spector, Laura; Leal-Lopes, Camila & Teixeira, André A. [Show all 17 contributors for this article] (2024). Correction to: Insights into next generation sequencing guided antibody selection strategies (Scientific Reports, (2023), 13, 1, (18370), 10.1038/s41598-023-45538-w). Scientific Reports. ISSN 2045-2322. 14(1). doi: 10.1038/s41598-024-53751-4.
• Erasmus, M. Frank; Ferrara, Fortunato; D’Angelo, Sara; Spector, Laura; Leal‑Lopes, Camila & Teixeira, André A. [Show all 17 contributors for this article] (2023). Author Correction: Insights into next generation sequencing guided antibody selection strategies (Scientific Reports, (2023), 13, 1, (18370), 10.1038/s41598-023-45538-w). Scientific Reports. ISSN 2045-2322. 13(1). doi: 10.1038/s41598-023-49214-x.
• Vu, Mai Ha; Akbar, Rahmad; Robert, Philippe; Sandve, Geir Kjetil Ferkingstad; Haug, Dag Trygve Truslew & Greiff, Victor (2023). Linguistically inspired roadmap for building biologically reliable protein language models. Nature Machine Intelligence. 5(5), p. 485–496. doi: 10.1038/s42256-023-00637-1.
• Doeland, Elin Martine; Sandve, Geir Kjetil Ferkingstad & Greiff, Victor (2022). Immunforsvaret har skjulte mønstre om sykdom og infeksjoner. Forskning.no. ISSN 1891-635X.
• Torgersen, Eivind; Geir Kjetil, Sandve; Greiff, Victor; Pavlovic, Milena & Scheffer, Lonneke (2022). Fra bare én blodprøve kan kunstig intelligens gi diagnose for mange ulike sykdommer. Titan.uio.no.
• Sandve, Geir Kjetil Ferkingstad; Greiff, Victor; Haff, Ingrid Hobæk & Vu, Mai Ha (2022). Deciphering the Immune System Through Linguistics-Inspired Statistical Machine Learning.
• Sandve, Geir Kjetil Ferkingstad & Greiff, Victor (2022). Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking. Bioinformatics. ISSN 1367-4803. 38(21), p. 4994–4996. doi: 10.1093/bioinformatics/btac612. Full text in Research Archive
• Greiff, Victor (2022). Predicting adaptive immunity using systems immunology and machine learning.
• Greiff, Victor (2022). Statistical and machine-learning analysis of adaptive immune specificity.
• Greiff, Victor (2022). AI-based prediction of the adaptive immune response.
• Greiff, Victor (2022). A Compact Vocabulary of Paratope-Epitope Interactions Enables Predictability of Antibody-Antigen Binding.
• Greiff, Victor (2022). Towards targeted computational and machine-learning- based antibody specificity and developability design.
• Greiff, Victor (2022). Machine-learning-based antibody design.
• Greiff, Victor (2022). Profiling the specificity of adaptive immune receptor repertoires.
• Greiff, Victor (2021). Towards predicting antibody-antigen at the paratope-epitope level using machine learning.
• Huang, Yu-Ning; Peng, Kerui; Popejoy, Alice B.; Hu, Jieting; Nowicki, Theodore Scott & Gold, Stefan M. [Show all 13 contributors for this article] (2021). Ancestral diversity is limited in published T cell receptor sequencing studies. Immunity. ISSN 1074-7613. 54(10), p. 2177–2179. doi: 10.1016/j.immuni.2021.09.015.
• Greiff, Victor (2021). Development of Machine Learning Methods for the Analysis, Prediction and Generation of Antibody Repertoires.
• Greiff, Victor (2021). Machine-learning-based design of antibody therapeutics.
• Greiff, Victor (2021). Latest techniques in validation of computational drug discovery.
• Greiff, Victor (2021). Computational and machine learning approaches for identifying antigen-specific information in adaptive immune receptor repertoires.
• Greiff, Victor (2021). Quantifying the specificity of adaptive immunity.
• Greiff, Victor (2021). Introduction to computational adaptive immune receptor repertoire (AIRR) immunogenomics.
• Greiff, Victor (2021). Future perspectives: from protein structure to function.
• Greiff, Victor (2021). Steps in data processing and analysis of adaptive immune receptor repertoires: best practices, pitfalls, and future directions.
• Greiff, Victor (2021). Mining the rules of adaptive immune repertoires in health and disease.
• Greiff, Victor (2021). Quantifying the specificity of adaptive immunity.
• Greiff, Victor (2021). Quantifying the specificity of adaptive immunity.
• 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.

• Greiff, Victor (2020). Systems immunology of adaptive immune receptor repertoires:  Deciphering immunological variation and specificity.
• Greiff, Victor (2020). Deep-learning based prediction of antibody-antigen interaction.
• Greiff, Victor (2020). Machine learning on sequence and structural adaptive immune receptor repertoire (AIRR) data .
• Greiff, Victor (2020). Quantifying the specificity of the adaptive immune response on the repertoire and sequence level.
• Greiff, Victor (2020). Systems immunology of immune receptor repertoires: Deciphering immunological specificity.
• Greiff, Victor (2020). Methods for mining the adaptive immune repertoire for SARS-CoV-2 diagnostics and therapeutics.

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