Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Andrés Pérez-Manríquez"'
Autor:
Jie Zhang, Søren D. Petersen, Tijana Radivojevic, Andrés Ramirez, Andrés Pérez-Manríquez, Eduardo Abeliuk, Benjamín J. Sánchez, Zak Costello, Yu Chen, Michael J. Fero, Hector Garcia Martin, Jens Nielsen, Jay D. Keasling, Michael K. Jensen
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve predicti
Externí odkaz:
https://doaj.org/article/12b7d69682c04fc38cfe252ad2aedc1b
Autor:
Michael Krogh Jensen, Jens Nielsen, Jie Zhang, Eduardo Abeliuk, Zak Costello, Andrés Ramirez, Andrés Pérez-Manríquez, Benjamin Sanchez, Søren D. Petersen, Jay D. Keasling, Hector Garcia Martin, Michael J. Fero, Tijana Radivojevic, Yu Chen
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Nature Communications
Nature communications, vol 11, iss 1
Zhang, J, Petersen, S D, Radivojevic, T, Ramirez, A, Pérez-Manríquez, A, Abeliuk, E, Sánchez, B J, Costello, Z, Chen, Y, Fero, M J, Martin, H G, Nielsen, J, Keasling, J D & Jensen, M K 2020, ' Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism ', Nature Communications, vol. 11, no. 1, 4880 . https://doi.org/10.1038/s41467-020-17910-1
Nature Communications
Nature communications, vol 11, iss 1
Zhang, J, Petersen, S D, Radivojevic, T, Ramirez, A, Pérez-Manríquez, A, Abeliuk, E, Sánchez, B J, Costello, Z, Chen, Y, Fero, M J, Martin, H G, Nielsen, J, Keasling, J D & Jensen, M K 2020, ' Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism ', Nature Communications, vol. 11, no. 1, 4880 . https://doi.org/10.1038/s41467-020-17910-1
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c