The role of machine learning to boost the bioenergy and biofuels conversion
The development and application of bioenergy and biofuels conversion technology can play
a significant role for the production of renewable and sustainable energy sources in the …
a significant role for the production of renewable and sustainable energy sources in the …
A critical review of machine-learning for “multi-omics” marine metabolite datasets
J Manochkumar, AK Cherukuri, RS Kumar… - Computers in Biology …, 2023 - Elsevier
During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics
datasets have been generated for a wide range of marine organisms, and even more are …
datasets have been generated for a wide range of marine organisms, and even more are …
Using machine learning as a surrogate model for agent-based simulations
In this proof-of-concept work, we evaluate the performance of multiple machine-learning
methods as surrogate models for use in the analysis of agent-based models (ABMs) …
methods as surrogate models for use in the analysis of agent-based models (ABMs) …
Artificial intelligence: a solution to involution of design–build–test–learn cycle
Highlights•DBTL for cell factory development faces involution without breakthrough.•
Machine learning can assist DBTL from genetic optimizations to fermentation controls.•The …
Machine learning can assist DBTL from genetic optimizations to fermentation controls.•The …
Machine learning for microalgae detection and utilization
H Ning, R Li, T Zhou - Frontiers in Marine Science, 2022 - frontiersin.org
Microalgae are essential parts of marine ecology, and they play a key role in species
balance. Microalgae also have significant economic value. However, microalgae are too …
balance. Microalgae also have significant economic value. However, microalgae are too …
Optimization and scale-up of fermentation processes driven by models
YH Du, MY Wang, LH Yang, LL Tong, DS Guo, XJ Ji - Bioengineering, 2022 - mdpi.com
In the era of sustainable development, the use of cell factories to produce various
compounds by fermentation has attracted extensive attention; however, industrial …
compounds by fermentation has attracted extensive attention; however, industrial …
Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction
Predicting bioproduction titers from microbial hosts has been challenging due to complex
interactions between microbial regulatory networks, stress responses, and suboptimal …
interactions between microbial regulatory networks, stress responses, and suboptimal …
Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges
MK Goshisht - ACS omega, 2024 - ACS Publications
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
[HTML][HTML] Advances in flux balance analysis by integrating machine learning and mechanism-based models
The availability of multi-omics data sets and genome-scale metabolic models for various
organisms provide a platform for modeling and analyzing genotype-to-phenotype …
organisms provide a platform for modeling and analyzing genotype-to-phenotype …
A general hybrid modeling framework for systems biology applications: Combining mechanistic knowledge with deep neural networks under the SBML standard
In this paper, a computational framework is proposed that merges mechanistic modeling
with deep neural networks obeying the Systems Biology Markup Language (SBML) …
with deep neural networks obeying the Systems Biology Markup Language (SBML) …