换一批Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.
第一作者：
Sang Mi,Lee
第一单位：
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
作者：
关键词
1D CNN, one-dimensional convolutional neural networkCancer patient-specific metabolic modelE-Flux2, E-Flux method combined with minimization of L2normEvaluationFBA, flux balance analysisGEM, genome-scale metabolic modelGIMME, Gene Inactivity Moderated by Metabolism and ExpressionGPR, gene-protein-reactionGenome-scale metabolic modelLAD, least absolute deviationMEM, model extraction methodMSM, model simulation methodMachine learningModel extraction methodModel simulation methodSPOT, Simplified Pearson cOrrelation with Transcriptomic datapFBA, parsimonious flux balance analysist-SNE, t-distributed stochastic neighbor embeddingtINIT, task-driven Integrative Network Inference for Tissues
DOI
10.1016/j.csbj.2022.06.027
PMID
35782748
发布时间
2022-07-16
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