| Designer exosomes produced by implanted cells intracerebrally deliver therapeutic cargo for Parkinson’s disease treatment R Kojima, D Bojar, G Rizzi, GCE Hamri, MD El-Baba, P Saxena, ... Nature communications 9 (1), 1305, 2018 | 537 | 2018 |
| Crystal structures of the phosphorylated BRI 1 kinase domain and implications for brassinosteroid signal initiation D Bojar, J Martinez, J Santiago, V Rybin, R Bayliss, M Hothorn The Plant Journal 78 (1), 31-43, 2014 | 159 | 2014 |
| Generalized extracellular molecule sensor platform for programming cellular behavior L Scheller, T Strittmatter, D Fuchs, D Bojar, M Fussenegger Nature Chemical Biology, 2018 | 126 | 2018 |
| A useful guide to lectin binding: machine-learning directed annotation of 57 unique lectin specificities D Bojar, L Meche, G Meng, W Eng, DF Smith, RD Cummings, LK Mahal ACS chemical biology 17 (11), 2993-3012, 2022 | 118 | 2022 |
| A CRISPR/Cas9-based central processing unit to program complex logic computation in human cells H Kim, D Bojar, M Fussenegger Proceedings of the National Academy of Sciences 116 (15), 7214-7219, 2019 | 77 | 2019 |
| Caffeine-inducible gene switches controlling experimental diabetes D Bojar, L Scheller, G Charpin-El Hamri, M Xie, M Fussenegger Nature Communications 9 (2318), 2018 | 74 | 2018 |
| Design and applications of a clamp for Green Fluorescent Protein with picomolar affinity S Hansen, J Stüber, P Ernst, A Koch, D Bojar, A Batyuk, A Plückthun Scientific Reports 7 (16292), 2017 | 55 | 2017 |
| Deep-learning resources for studying glycan-mediated host-microbe interactions D Bojar, RK Powers, DM Camacho, JJ Collins Cell Host & Microbe 29 (1), 132-144. e3, 2021 | 47 | 2021 |
| Using graph convolutional neural networks to learn a representation for glycans R Burkholz, J Quackenbush, D Bojar Cell Reports 35 (11), 109251, 2021 | 27 | 2021 |
| LectinOracle: a generalizable deep learning model for lectin–glycan binding prediction J Lundstrøm, E Korhonen, F Lisacek, D Bojar Advanced Science 9 (1), 2103807, 2022 | 26 | 2022 |
| Glycowork: A Python package for glycan data science and machine learning L Thomès, R Burkholz, D Bojar Glycobiology, 2021 | 25 | 2021 |
| Glycoinformatics in the artificial intelligence era D Bojar, F Lisacek Chemical Reviews 122 (20), 15971-15988, 2022 | 24 | 2022 |
| Design of synthetic promoters for gene circuits in mammalian cells P Saxena, D Bojar, M Fussenegger Mammalian synthetic promoters, 263-273, 2017 | 21 | 2017 |
| The role of fucose-containing glycan motifs across taxonomic kingdoms L Thomès, D Bojar Frontiers in Molecular Biosciences 8, 755577, 2021 | 19 | 2021 |
| The Role of Protein Engineering in Biomedical Applications of Mammalian Synthetic Biology D Bojar, M Fussenegger Small, 2019 | 18 | 2019 |
| Generation of glucose-sensitive insulin-secreting beta-like cells from human embryonic stem cells by incorporating a synthetic lineage-control network P Saxena, D Bojar, H Zulewski, M Fussenegger Journal of Biotechnology, 2017 | 18 | 2017 |
| Using Natural Language Processing to Learn the Grammar of Glycans D Bojar, D Camacho, J Collins bioRxiv, 2020 | 13 | 2020 |
| The best of both worlds: reaping the benefits from mammalian and bacterial therapeutic circuits. D Bojar, M Fussenegger Current Opinion in Chemical Biology 34, 11-19, 2016 | 12 | 2016 |
| Purity by design: Reducing impurities in bioproduction by stimulus-controlled global translational downregulation of non-product proteins D Bojar, T Fuhrer, M Fussenegger Metabolic Engineering, 2018 | 11 | 2018 |
| GlycoDraw: a python implementation for generating high-quality glycan figures J Lundstrøm, J Urban, L Thomès, D Bojar Glycobiology 33 (11), 927-934, 2023 | 8 | 2023 |
Martin FusseneggerETH ZurichVerified email at bsse.ethz.ch
