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References from The era of big data: Genome-scale modelling meets machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1
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Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism
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10.1007/s00253-017-8612-y · External reference
A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism
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Application of a curated genome-scale metabolic model of CHO DG44 to an industrial fed-batch process
10.1016/j.ymben.2018.09.009 · 2019 · External reference
10.1101/2020.01.27.921296
10.1101/2020.01.27.921296 · External reference
Enzyme capacity-based genome scale modelling of CHO cells
10.1016/j.ymben.2020.04.005 · 2020 · External reference
Modeling Hybridoma Cell Metabolism Using a GenericGenome-Scale Metabolic Model ofMus musculus
10.1021/bp0498138 · 2008 · External reference
iMM1865: A New Reconstruction of Mouse Genome-Scale Metabolic Model
10.1038/s41598-020-63235-w · 2020 · External reference
10.1073/pnas.1307797110
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Dynamic model for CHO cell engineering
10.1016/j.jbiotec.2012.01.009 · 2012 · External reference
10.1002/biot.201100052
10.1002/biot.201100052 · External reference
A Single Dynamic Metabolic Model Can Describe mAb Producing CHO Cell Batch and Fed-Batch Cultures on Different Culture Media
10.1371/journal.pone.0136815 · 2015 · External reference
10.1038/s41467-019-13867-y
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10.1038/nbt.1614
10.1038/nbt.1614 · External reference
lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites
10.1371/journal.pcbi.1005513 · 2017 · External reference
Hatzimanikatis, redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models
10.1371/journal.pcbi.1005444 · 2017 · External reference
On elementary flux modes in biochemical reaction systems at steady state
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10.1371/journal.pcbi.1000082 · External reference
Optimization based automated curation of metabolic reconstructions
10.1186/1471-2105-8-212 · 2007 · External reference
10.1093/bioinformatics/btu321
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MetaCyc: a multiorganism database of metabolic pathways and enzymes
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Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions
10.1002/bit.27025 · 2019 · External reference
A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models
2019 · External reference
Thermodynamics-based metabolic flux analysis
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An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells
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10.1002/bit.10803
10.1002/bit.10803 · External reference
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems
10.1016/j.ymben.2005.08.003 · 2006 · External reference
k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design
10.1371/journal.pcbi.1003487 · 2014 · External reference
Large-scale identification of genetic design strategies using local search
10.1038/msb.2009.57 · 2009 · External reference
10.1371/journal.pcbi.1006835
10.1371/journal.pcbi.1006835 · External reference
Determination of growth-coupling strategies and their underlying principles
10.1186/s12859-019-2946-7 · 2019 · External reference
OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains
2010 · External reference
OptForce: An optimization procedure for identifying all genetic manipulations leading to targeted overproductions
10.1371/journal.pcbi.1000744 · 2010 · External reference
OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs
10.1016/j.mec.2019.e00087 · 2019 · External reference
Metabolic model guided strain design of cyanobacteria
10.1016/j.copbio.2019.08.011 · 2020 · External reference
10.1016/j.ymben.2017.06.008
10.1016/j.ymben.2017.06.008 · External reference
Engineering of E. coli inherent fatty acid biosynthesis capacity to increase octanoic acid production
10.1186/s13068-018-1078-z · 2018 · External reference
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information
10.1038/msb4100155 · 2007 · External reference
10.1016/j.mec.2019.e00088
10.1016/j.mec.2019.e00088 · External reference
Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris
10.1186/s12918-017-0408-2 · 2017 · External reference
Multiplex secretome engineering enhances recombinant protein production and purity
10.1038/s41467-020-15866-w · 2020 · External reference
CHO cell productivity improvement by genome-scale modeling and pathway analysis: Application to feed supplements
2020 · External reference
Optimizing amino acid composition of CHO cell culture media for a fusion protein production
10.1016/j.procbio.2011.03.014 · 2011 · External reference
10.1016/j.ymben.2019.02.005
10.1016/j.ymben.2019.02.005 · External reference
10.1016/j.ymben.2017.01.008
10.1016/j.ymben.2017.01.008 · External reference
Concomitant consumption of glucose and lactate: A novel batch production process for CHO cells
10.1016/j.bej.2019.107358 · 2019 · External reference
A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production
10.1007/s00449-020-02332-6 · 2020 · External reference
Dynamic metabolic models of CHO cell cultures through minimal sets of elementary flux modes
10.1016/j.jbiotec.2012.05.005 · 2013 · External reference
Dynamic metabolic flux analysis using B-splines to study the effects of temperature shift on CHO cell metabolism
10.1016/j.meteno.2015.06.001 · 2015 · External reference
Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli
10.3390/metabo10050198 · 2020 · External reference
Dynamic flux balance analysis for predicting biomass growth and ethanol production in yeast fed-batch cultures
10.1016/j.ifacol.2018.03.107 · 2018 · External reference
Dynamic metabolic modelling of overproduced protein secretion in Streptomyces lividans using adaptive DFBA
10.1186/s12866-019-1591-7 · 2019 · External reference
Contextualized genome-scale model unveils high-order metabolic effects of the specific growth rate and oxygenation level in recombinant Pichia pastoris
10.1016/j.mec.2019.e00103 · 2019 · External reference
Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources
10.1111/1751-7915.12871 · 2018 · External reference
10.1016/j.neuroimage.2017.06.061
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Machine learning algorithm validation with a limited sample size
10.1371/journal.pone.0224365 · 2019 · External reference
Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap
10.1016/j.csda.2009.04.009 · 2009 · External reference
On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning
10.1007/s41664-018-0068-2 · 2018 · External reference
10.1007/s11306-007-0081-3
10.1007/s11306-007-0081-3 · External reference
Multivariate Analysis in Metabolomics
2013 · External reference
10.1186/1471-2164-7-142
10.1186/1471-2164-7-142 · External reference
10.1016/j.cell.2018.05.061
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DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
10.1186/s13059-019-1837-6 · 2019 · External reference
DrImpute: imputing dropout events in single cell RNA sequencing data
10.1186/s12859-018-2226-y · 2018 · External reference
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10.1038/s41592-018-0033-z · External reference
Feature selection for high-dimensional data
10.1007/s13748-015-0080-y · 2016 · External reference
10.1016/j.inffus.2018.11.008
10.1016/j.inffus.2018.11.008 · External reference
A survey on feature selection methods
10.1016/j.compeleceng.2013.11.024 · 2014 · External reference
An introduction to variable and feature selection
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Unresolved reference
External reference
Benchmark for filter methods for feature selection in high-dimensional classification data
10.1016/j.csda.2019.106839 · 2020 · External reference
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Regression Shrinkage and Selection Via the Lasso
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Sparse Discriminant Analysis
10.1198/tech.2011.08118 · 2011 · External reference
10.1023/a:1012487302797
10.1023/a:1012487302797 · External reference
Embedded feature selection accounting for unknown data heterogeneity
10.1016/j.eswa.2018.11.006 · 2019 · External reference
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
10.1162/neco_a_00537 · 2013 · External reference
Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation
10.1186/1752-0509-3-30 · 2009 · External reference
Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media
10.1016/j.chemolab.2014.02.003 · 2014 · External reference
10.1039/c5mb00828j
10.1039/c5mb00828j · External reference
Dynamic elementary mode modelling of non-steady state flux data
10.1186/s12918-018-0589-3 · 2018 · External reference
A principal components method constrained by elementary flux modes: analysis of flux data sets
10.1186/s12859-016-1063-0 · 2016 · External reference
Principal metabolic flux mode analysis
10.1093/bioinformatics/bty049 · 2018 · External reference
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Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics
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Lung Cancer Stigma across the Social Network: Patient and Caregiver Perspectives
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Predictive analytics of environmental adaptability in multi-omic network models
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10.1038/nbt.3703 · External reference
Metabolic remodelling of glucose, fatty acid and redox pathways in the heart of type 2 diabetic mice
10.1113/jp276824 · 2020 · External reference
The poly-omics of ageing through individual-based metabolic modelling
10.1186/s12859-018-2383-z · 2018 · External reference
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
10.1093/bioinformatics/btx562 · 2017 · External reference
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Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders
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A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB
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MCR-ALS on metabolic networks: Obtaining more meaningful pathways
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Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review
2016 · External reference
10.1016/j.cell.2019.04.016
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Machine and deep learning meet genome-scale metabolic modeling
10.1371/journal.pcbi.1007084 · 2019 · External reference
Machine learning based analyses on metabolic networks supports high-throughput knockout screens
10.1186/1752-0509-2-67 · 2008 · External reference
Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information
10.1186/1471-2105-10-290 · 2009 · External reference
Machine learning methods for metabolic pathway prediction
10.1186/1471-2105-11-15 · 2010 · External reference
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SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis
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Metabolomic profiling of CHO fed-batch growth phases at 10, 100, and 1,000 L
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A biochemically-interpretable machine learning classifier for microbial GWAS
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Predicting Growth Conditions from Internal Metabolic Fluxes in an In-Silico Model of E. coli
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Unresolved reference
External reference
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An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features
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Global transcription machinery engineering: A new approach for improving cellular phenotype
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The metabolic background is a global player in Saccharomyces gene expression epistasis
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Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data
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DMFA-based operation model for fermentation processes
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DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis
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An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals
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Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression
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Active Learning with Support Vector Machine Applied to Gene Expression Data for Cancer Classification
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Support vector machine approach for protein subcellular localization prediction
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Support Vector Machines for predicting protein structural class
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Naïve Bayes for microRNA target predictions—machine learning for microRNA targets
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Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites
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Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles
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Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules
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A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
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Network-based prediction of human tissue-specific metabolism
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