A versatile active learning workflow for optimization of genetic and metabolic networks
oleh: Amir Pandi, Christoph Diehl, Ali Yazdizadeh Kharrazi, Scott A. Scholz, Elizaveta Bobkova, Léon Faure, Maren Nattermann, David Adam, Nils Chapin, Yeganeh Foroughijabbari, Charles Moritz, Nicole Paczia, Niña Socorro Cortina, Jean-Loup Faulon, Tobias J. Erb
| Format: | Article |
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| Diterbitkan: | Nature Portfolio 2022-07-01 |
Deskripsi
Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.