Machine learning for metabolic engineering: A review

Volume: 63, Pages: 34 - 60
Published: Jan 1, 2021
Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources,...
Paper Details
Title
Machine learning for metabolic engineering: A review
Published Date
Jan 1, 2021
Volume
63
Pages
34 - 60
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.