DeepTFactor: A deep learning-based tool for the prediction of transcription factors
GB Kim, Y Gao, BO Palsson… - Proceedings of the …, 2021 - National Acad Sciences
A transcription factor (TF) is a sequence-specific DNA-binding protein that modulates the
transcription of a set of particular genes, and thus regulates gene expression in the cell. TFs …
transcription of a set of particular genes, and thus regulates gene expression in the cell. TFs …
Addressing uncertainty in genome-scale metabolic model reconstruction and analysis
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful
systems biology approach, with applications ranging from basic understanding of genotype …
systems biology approach, with applications ranging from basic understanding of genotype …
[HTML][HTML] Recent advances in microbial production of medium chain fatty acid from renewable carbon resources: A comprehensive review
Microbial production of medium chain length fatty acids (MCFAs) from renewable resources
is becoming increasingly important in establishing a sustainable and clean chemical …
is becoming increasingly important in establishing a sustainable and clean chemical …
Genome-scale modeling of yeast metabolism: retrospectives and perspectives
Yeasts have been widely used for production of bread, beer and wine, as well as for
production of bioethanol, but they have also been designed as cell factories to produce …
production of bioethanol, but they have also been designed as cell factories to produce …
Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution
Being integral primary producers in diverse ecosystems, microalgal genomes could be
mined for ecological insights, but representative genome sequences are lacking for many …
mined for ecological insights, but representative genome sequences are lacking for many …
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 …
Enzyme commission number prediction and benchmarking with hierarchical dual-core multitask learning framework
Enzyme commission (EC) numbers, which associate a protein sequence with the
biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme …
biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme …
In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering
JL Faulon, L Faure - Current Opinion in Chemical Biology, 2021 - Elsevier
Among the main learning methods reviewed in this study and used in synthetic biology and
metabolic engineering are supervised learning, reinforcement and active learning, and in …
metabolic engineering are supervised learning, reinforcement and active learning, and in …
Design of synthetic promoters for cyanobacteria with generative deep-learning model
E Seo, YN Choi, YR Shin, D Kim… - Nucleic Acids …, 2023 - academic.oup.com
Deep generative models, which can approximate complex data distribution from large
datasets, are widely used in biological dataset analysis. In particular, they can identify and …
datasets, are widely used in biological dataset analysis. In particular, they can identify and …
An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites
In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable
variability across scales from a single tree-crown to the entire forest ecosystem. Such …
variability across scales from a single tree-crown to the entire forest ecosystem. Such …