| Engineering microbial consortia by division of labor GW Roell, J Zha, RR Carr, MA Koffas, SS Fong, YJ Tang Microbial cell factories 18, 1-11, 2019 | 234 | 2019 |
| A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630 GW Roell, RR Carr, T Campbell, Z Shang, WR Henson, JJ Czajka, ... Metabolic engineering 55, 120-130, 2019 | 50 | 2019 |
| Generative artificial intelligence GPT-4 accelerates knowledge mining and machine learning for synthetic biology Z Xiao, W Li, H Moon, GW Roell, Y Chen, YJ Tang ACS synthetic biology 12 (10), 2973-2982, 2023 | 22 | 2023 |
| A comparative evaluation of machine learning algorithms for predicting syngas fermentation outcomes GW Roell, A Sathish, N Wan, Q Cheng, Z Wen, YJ Tang, FS Bao Biochemical Engineering Journal 186, 108578, 2022 | 8 | 2022 |
| A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism GW Roell, C Schenk, WE Anthony, RR Carr, A Ponukumati, J Kim, ... ACS synthetic biology 12 (6), 1632-1644, 2023 | 5 | 2023 |
| Elucidation of triacylglycerol catabolism in Yarrowia lipolytica: How cells balance acetyl-CoA and excess reducing equivalents AM Worland, Z Han, J Maruwan, Y Wang, ZY Du, YJ Tang, WW Su, ... Metabolic Engineering 85, 1-13, 2024 | 2 | 2024 |
| Impact: The Industrial Microbiology Publication and AI Crowdsourced Toolbox Y Sun, Z Xiao, H Moon, J Czajka, Y Chen, Y Tang, G Roell 2024 AIChE Annual Meeting, 2024 | | 2024 |
| The Development of Metabolic Models and Machine Learning Methods for Biofuel-Producing Bacteria G Roell Washington University in St. Louis, 2023 | | 2023 |
| A Genome Scale Model for Prediction of Growth Rates and Fluxes for Rhodococcus Opacus PD630 Metabolism G Roell, C Schenk, W Anthony, R Carr, G Dantas, Y Tang, TS Moon, ... 2022 AIChE Annual Meeting, 2022 | | 2022 |
| A Comparative Evaluation of Machine Learning Algorithms in Predicting Syngas Fermentation Outcomes Using Limited Experimental Data G Roell, A Sathish, N Wan, Z Wen, Y Tang, FS Bao 2022 AIChE Annual Meeting, 2022 | | 2022 |
