| The role of photorespiration during the evolution of C4 photosynthesis in the genus Flaveria J Mallmann, D Heckmann, A Bräutigam, MJ Lercher, APM Weber, ... Elife 3, e02478, 2014 | 216 | 2014 |
| Predicting C4 photosynthesis evolution: modular, individually adaptive steps on a Mount Fuji fitness landscape D Heckmann, S Schulze, A Denton, U Gowik, P Westhoff, APM Weber, ... Cell 153 (7), 1579-1588, 2013 | 199 | 2013 |
| Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models D Heckmann, CJ Lloyd, N Mih, Y Ha, DC Zielinski, ZB Haiman, ... Nature communications 9 (1), 5252, 2018 | 196 | 2018 |
| Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance ES Kavvas, E Catoiu, N Mih, JT Yurkovich, Y Seif, N Dillon, D Heckmann, ... Nature communications 9 (1), 4306, 2018 | 189 | 2018 |
| Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra D Heckmann, U Schlüter, APM Weber Molecular plant 10 (6), 878-890, 2017 | 108 | 2017 |
| Cellular responses to reactive oxygen species are predicted from molecular mechanisms L Yang, N Mih, A Anand, JH Park, J Tan, JT Yurkovich, JM Monk, CJ Lloyd, ... Proceedings of the National Academy of Sciences 116 (28), 14368-14373, 2019 | 103 | 2019 |
| A biochemically-interpretable machine learning classifier for microbial GWAS ES Kavvas, L Yang, JM Monk, D Heckmann, BO Palsson Nature communications 11 (1), 2580, 2020 | 78 | 2020 |
| Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers D Heckmann, A Campeau, CJ Lloyd, PV Phaneuf, Y Hefner, ... Proceedings of the National Academy of Sciences 117 (37), 23182-23190, 2020 | 76 | 2020 |
| Deep learning allows genome-scale prediction of Michaelis constants from structural features A Kroll, MKM Engqvist, D Heckmann, MJ Lercher PLoS biology 19 (10), e3001402, 2021 | 74 | 2021 |
| Independent component analysis recovers consistent regulatory signals from disparate datasets AV Sastry, A Hu, D Heckmann, S Poudel, E Kavvas, BO Palsson PLoS computational biology 17 (2), e1008647, 2021 | 40 | 2021 |
| C4 photosynthesis evolution: the conditional Mt. Fuji D Heckmann Current Opinion in Plant Biology 31, 149-154, 2016 | 27 | 2016 |
| Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity PV Phaneuf, JT Yurkovich, D Heckmann, M Wu, TE Sandberg, ZA King, ... BMC genomics 21, 1-16, 2020 | 25 | 2020 |
| BLISTER Regulates Polycomb-Target Genes, Represses Stress-Regulated Genes and Promotes Stress Responses in Arabidopsis thaliana JA Kleinmanns, N Schatlowski, D Heckmann, D Schubert Frontiers in Plant Science 8, 1530, 2017 | 21 | 2017 |
| Combining genetic and evolutionary engineering to establish C4 metabolism in C3 plants Y Li, D Heckmann, MJ Lercher, VG Maurino Journal of Experimental Botany 68 (2), 117-125, 2017 | 19 | 2017 |
| Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat Commun 9: 4306 ES Kavvas, E Catoiu, N Mih, JT Yurkovich, Y Seif, N Dillon, D Heckmann, ... | 17 | 2018 |
| A generic avian physiologically-based kinetic (PBK) model and its application in three bird species V Baier, A Paini, S Schaller, CG Scanes, AJ Bone, M Ebeling, TG Preuss, ... Environment international 169, 107547, 2022 | 16 | 2022 |
| Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates D Heckmann, DC Zielinski, BO Palsson Nature Communications 9 (1), 5270, 2018 | 15 | 2018 |
| Quantitative comparison of avian and mammalian physiologies for parameterization of physiologically based kinetic models CG Scanes, J Witt, M Ebeling, S Schaller, V Baier, AJ Bone, TG Preuss, ... Frontiers in physiology 13, 858386, 2022 | 14 | 2022 |
| Energetic basis for bird ontogeny and egg-laying applied to the bobwhite quail N Marn, K Lika, S Augustine, B Goussen, M Ebeling, D Heckmann, ... Conservation physiology 10 (1), coac063, 2022 | 13 | 2022 |
| Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat Commun. 2018; 9 (1): 5252 D Heckmann, CJ Lloyd, N Mih, Y Ha, DC Zielinski, ZB Haiman, ... | 13 | |