Works matching DE "METABOLIC models"
Results: 1064
Unraveling the metabolic landscape of Exophiala spinifera strain FM: Model reconstruction, insights into biodesulfurization and beyond.
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- PLoS ONE, 2025, v. 20, n. 1, p. 1, doi. 10.1371/journal.pone.0317796
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Grain Yield Prediction Based on the Metabolic Grey - Markov Integration Model.
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- Journal of Grey System, 2021, v. 33, n. 2, p. 95
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Substrate type and CO<sub>2</sub> addition significantly influence succinic acid production of Basfia succiniciproducens.
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- Biotechnology Letters, 2023, v. 45, n. 9, p. 1133, doi. 10.1007/s10529-023-03406-7
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Systematically gap-filling the genome-scale metabolic model of CHO cells.
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- Biotechnology Letters, 2021, v. 43, n. 1, p. 73, doi. 10.1007/s10529-020-03021-w
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Local metabolic hypothesis is not sufficient to explain coronary autoregulatory behavior.
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- Basic Research in Cardiology, 2018, v. 113, n. 5, p. 1, doi. 10.1007/s00395-018-0691-0
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Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control.
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- Bioinformatics, 2024, v. 40, n. 12, p. 1, doi. 10.1093/bioinformatics/btae691
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gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python.
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- Bioinformatics, 2024, v. 40, n. 6, p. 1, doi. 10.1093/bioinformatics/btae318
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PyCoMo: a python package for community metabolic model creation and analysis.
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- Bioinformatics, 2024, v. 40, n. 4, p. 1, doi. 10.1093/bioinformatics/btae153
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Shu: visualization of high-dimensional biological pathways.
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- Bioinformatics, 2024, v. 40, n. 3, p. 1, doi. 10.1093/bioinformatics/btae140
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Interpreting protein abundance in Saccharomyces cerevisiae through relational learning.
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- Bioinformatics, 2024, v. 40, n. 2, p. 1, doi. 10.1093/bioinformatics/btae050
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Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution.
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- Bioinformatics, 2024, v. 40, n. 1, p. 1, doi. 10.1093/bioinformatics/btae003
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TranSyT, an innovative framework for identifying transport systems.
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- Bioinformatics, 2023, v. 39, n. 8, p. 1, doi. 10.1093/bioinformatics/btad466
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COVRECON: automated integration of genome- and metabolome-scale network reconstruction and data-driven inverse modeling of metabolic interaction networks.
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- Bioinformatics, 2023, v. 39, n. 7, p. 1, doi. 10.1093/bioinformatics/btad397
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Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling.
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- Bioinformatics, 2023, v. 39, n. 5, p. 1, doi. 10.1093/bioinformatics/btad186
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kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase.
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- Bioinformatics, 2023, v. 39, n. 4, p. 1, doi. 10.1093/bioinformatics/btad110
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ecmtool: fast and memory-efficient enumeration of elementary conversion modes.
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- Bioinformatics, 2023, v. 39, n. 3, p. 1, doi. 10.1093/bioinformatics/btad095
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Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.
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- Bioinformatics, 2023, v. 39, n. 3, p. 1, doi. 10.1093/bioinformatics/btad089
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StrainDesign: a comprehensive Python package for computational design of metabolic networks.
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- Bioinformatics, 2022, v. 38, n. 21, p. 4981, doi. 10.1093/bioinformatics/btac632
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gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs.
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- Bioinformatics, 2022, v. 38, n. 14, p. 3657, doi. 10.1093/bioinformatics/btac376
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CobraMod: a pathway-centric curation tool for constraint-based metabolic models.
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- Bioinformatics, 2022, v. 38, n. 9, p. 2654, doi. 10.1093/bioinformatics/btac119
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MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome‐metabolome data.
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- Bioinformatics, 2022, v. 38, n. 6, p. 1615, doi. 10.1093/bioinformatics/btac003
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COBREXA.jl: constraint-based reconstruction and exascale analysis.
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- Bioinformatics, 2022, v. 38, n. 4, p. 1171, doi. 10.1093/bioinformatics/btab782
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ORT: a workflow linking genome-scale metabolic models with reactive transport codes.
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- Bioinformatics, 2022, v. 38, n. 3, p. 778, doi. 10.1093/bioinformatics/btab753
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Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction.
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- Bioinformatics, 2022, v. 38, n. 2, p. 487, doi. 10.1093/bioinformatics/btab647
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PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes.
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- Bioinformatics, 2022, v. 38, n. 2, p. 364, doi. 10.1093/bioinformatics/btab547
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Visual exploration of large metabolic models.
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- Bioinformatics, 2021, v. 37, n. 23, p. 4460, doi. 10.1093/bioinformatics/btab335
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Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data.
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- Bioinformatics, 2021, v. 37, n. 20, p. 3546, doi. 10.1093/bioinformatics/btab324
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M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.
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- Bioinformatics, 2021, v. 37, n. 17, p. 2785, doi. 10.1093/bioinformatics/btab060
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MEWpy: a computational strain optimization workbench in Python.
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- Bioinformatics, 2021, v. 37, n. 16, p. 2494, doi. 10.1093/bioinformatics/btab013
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Predicting Nash equilibria for microbial metabolic interactions.
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- Bioinformatics, 2020, v. 36, n. 24, p. 5649, doi. 10.1093/bioinformatics/btaa1014
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A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data.
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- Bioinformatics, 2020, v. 36, p. i787, doi. 10.1093/bioinformatics/btaa841
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DeepNOG: fast and accurate protein orthologous group assignment.
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- Bioinformatics, 2020, v. 36, n. 22/23, p. 5304, doi. 10.1093/bioinformatics/btaa1051
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MinReact: a systematic approach for identifying minimal metabolic networks.
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- Bioinformatics, 2020, v. 36, n. 15, p. 4309, doi. 10.1093/bioinformatics/btaa497
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GEMtractor: extracting views into genome-scale metabolic models.
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- Bioinformatics, 2020, v. 36, n. 10, p. 3281, doi. 10.1093/bioinformatics/btaa068
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On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models.
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- Bioinformatics, 2020, v. 36, n. 6, p. 1986, doi. 10.1093/bioinformatics/btz832
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Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models.
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- Bioinformatics, 2020, v. 36, n. 6, p. 1823, doi. 10.1093/bioinformatics/btz813
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ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models.
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- Bioinformatics, 2020, v. 36, n. 5, p. 1644, doi. 10.1093/bioinformatics/btz761
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A scalable method for parameter identification in kinetic models of metabolism using steady-state data.
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- Bioinformatics, 2019, v. 35, n. 24, p. 5216, doi. 10.1093/bioinformatics/btz445
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Parameter balancing: consistent parameter sets for kinetic metabolic models.
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- Bioinformatics, 2019, v. 35, n. 19, p. 3857, doi. 10.1093/bioinformatics/btz129
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Gsmodutils: a python based framework for test-driven genome scale metabolic model development.
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- Bioinformatics, 2019, v. 35, n. 18, p. 3397, doi. 10.1093/bioinformatics/btz088
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MCS 2 : minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks.
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- Bioinformatics, 2019, v. 35, n. 14, p. i615, doi. 10.1093/bioinformatics/btz393
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Linking metabolic network features to phenotypes using sparse group lasso.
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- Bioinformatics, 2017, v. 33, n. 21, p. 3445, doi. 10.1093/bioinformatics/btx427
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Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models.
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- Bioinformatics, 2017, v. 33, n. 15, p. 2416, doi. 10.1093/bioinformatics/btx185
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Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.
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- Bioinformatics, 2017, v. 33, n. 15, p. 2345, doi. 10.1093/bioinformatics/btx171
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Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.
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- Bioinformatics, 2017, v. 33, n. 7, p. 1096, doi. 10.1093/bioinformatics/btw731
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Representation and inference of cellular architecture for metabolic reconstruction and modeling.
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- Bioinformatics, 2016, v. 32, n. 7, p. 1074, doi. 10.1093/bioinformatics/btv702
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M-path: a compass for navigating potential metabolic pathways.
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- Bioinformatics, 2015, v. 31, n. 6, p. 905, doi. 10.1093/bioinformatics/btu750
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TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm.
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- Bioinformatics, 2015, v. 31, n. 6, p. 897, doi. 10.1093/bioinformatics/btu733
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Estimating biological elementary flux modes that decompose a flux distribution by the minimal branching property.
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- Bioinformatics, 2014, v. 30, n. 22, p. 3232, doi. 10.1093/bioinformatics/btu529
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MetDraw: automated visualization of genome-scale metabolic network reconstructions and high-throughput data.
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- Bioinformatics, 2014, v. 30, n. 9, p. 1327, doi. 10.1093/bioinformatics/btt758
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