| Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure J Zrimec, CS Börlin, F Buric, AS Muhammad, R Chen, V Siewers, ... Nature communications 11 (1), 6141, 2020 | 148 | 2020 |
| metaGEM: reconstruction of genome scale metabolic models directly from metagenomes F Zorrilla, F Buric, KR Patil, A Zelezniak Nucleic acids research 49 (21), e126-e126, 2021 | 89 | 2021 |
| Learning the regulatory code of gene expression J Zrimec, F Buric, M Kokina, V Garcia, A Zelezniak Frontiers in Molecular Biosciences 8, 673363, 2021 | 37 | 2021 |
| Learning deep representations of enzyme thermal adaptation G Li, F Buric, J Zrimec, S Viknander, J Nielsen, A Zelezniak, ... Protein Science 31 (12), e4480, 2022 | 32 | 2022 |
| Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure. Nat Commun 2020; 11: 6141 J Zrimec, CS Börlin, F Buric, AS Muhammad, R Chen, V Siewers, ... doi. org/10.1038/s41467-020-19921-4, 0 | 7 | |
| Pattern formation and chemical evolution in extended Gray-Scott models F Buric | 5 | 2014 |
| The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost F Buric, S Viknander, X Fu, O Lemke, J Zrimec, L Szyrwiel, M Mueleder, ... bioRxiv, 2023.10. 02.560091, 2023 | 4 | 2023 |
| Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra F Buric, J Zrimec, A Zelezniak Patterns 1 (9), 2020 | 3 | 2020 |
| Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost F Buric, S Viknander, X Fu, O Lemke, OG Carmona, J Zrimec, L Szyrwiel, ... Protein Science 34 (1), e5239, 2025 | | 2025 |
| Mapping the Proteome with Data-Driven Methods: A Cycle of Measurement, Modeling, Hypothesis Generation, and Engineering F Buric PQDT-Global, 2021 | | 2021 |
