Genome-scale Metabolic Model Guided Subtyping Lung Cancer towards Personalized Diagnosis
Tài liệu tham khảo
Agren, 2012, Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT, PLoS Comput Biol, 8, 10.1371/journal.pcbi.1002518
Becker, 2008, Context-specific metabolic networks are consistent with experiments, PLoS Comput Biol, 4, 10.1371/journal.pcbi.1000082
Brunk, 2018, Recon3D enables a three-dimensional view of gene variation in human metabolism, Nature biotechnology, 36, 272, 10.1038/nbt.4072
Cai, 2015, Classification of lung cancer using ensemble-based feature selection and machine learning methods, Molecular BioSystems, 11, 791, 10.1039/C4MB00659C
Callejon-Leblic, 2016, Metabolic profiling of potential lung cancer biomarkers using bronchoalveolar lavage fluid and the integrated direct infusion/gas chromatography mass spectrometry platform, Journal of proteomics, 145, 197, 10.1016/j.jprot.2016.05.030
Chan, 2015, Targeted therapy for non-small cell lung cancer: current standards and the promise of the future, Translational lung cancer research, 4, 36
Chung, 2012, Computational codon optimization of synthetic gene for protein expression, BMC Systems Biology, 6, 1, 10.1186/1752-0509-6-134
Colijn, 2009, Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production, PLoS Comput Biol, 5, 10.1371/journal.pcbi.1000489
Damiani, 2017, A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The Warburg effect, PLoS computational biology, 13, 10.1371/journal.pcbi.1005758
Furey, 2000, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 906, 10.1093/bioinformatics/16.10.906
Ghaffari, 2015, Cancer metabolism: a modeling perspective, Frontiers in physiology, 6, 382, 10.3389/fphys.2015.00382
Golub, 1999, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, science, 286, 531, 10.1126/science.286.5439.531
Gudmundsson, 2010, Computationally efficient flux variability analysis, BMC Bioinformatics, 11, 2
Hanahan, 2011, Hallmarks of cancer: the next generation, cell, 144, 646, 10.1016/j.cell.2011.02.013
Heirendt, 2019, Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0’, Nature protocols, 14, 639, 10.1038/s41596-018-0098-2
Hsu, 2008, Cancer cell metabolism: Warburg and beyond, Cell, 134, 703, 10.1016/j.cell.2008.08.021
Huang, 2018, Applications of support vector machine (SVM) learning in cancer genomics, Cancer Genomics-Proteomics, 15, 41
Jemal, 2011, Global cancer statistics, CA: a cancer journal for clinicians, 61, 69
Kim, 2016, Weighted K-means support vector machine for cancer prediction, Springerplus, 5, 1, 10.1186/s40064-016-2677-4
Kim, 2017, Meta-analytic support vector machine for integrating multiple omics data, BioData mining, 10, 1
Kuepfer, 2005, Metabolic functions of duplicate genes in Saccharomyces cerevisiae, Genome research, 15, 1421, 10.1101/gr.3992505
Lewis, 2021, Personalized genome-scale metabolic models identify targets of redox metabolism in radiation-resistant tumors, Cell Systems, 12, 68, 10.1016/j.cels.2020.12.001
Lewis, 2010, Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models, Molecular systems biology, 6, 390, 10.1038/msb.2010.47
Li, 2004, A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression, Bioinformatics, 20, 2429, 10.1093/bioinformatics/bth267
Lin, 2017, Machine learning and systems genomics approaches for multi-omics data, Biomarker research, 5, 1, 10.1186/s40364-017-0082-y
Love, 2014, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome biology, 15, 1, 10.1186/s13059-014-0550-8
Model, 2001, Feature selection for DNA methylation based cancer classification, Bioinformatics, 17, S157, 10.1093/bioinformatics/17.suppl_1.S157
Moler, 2000, Analysis of molecular profile data using generative and discriminative methods, Physiological genomics, 4, 109, 10.1152/physiolgenomics.2000.4.2.109
Moreno, 2018, Metabolomic profiling of human lung tumor tissues–nucleotide metabolism as a candidate for therapeutic interventions and biomarkers, Molecular oncology, 12, 1778, 10.1002/1878-0261.12369
Nanda, 2020, Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS Cov2 infection, bioRxiv
Nilsson, 2017, Genome scale metabolic modeling of cancer, Metabolic engineering, 43, 103, 10.1016/j.ymben.2016.10.022
Podolsky, 2016, Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels, Asian Pacific Journal of Cancer Prevention, 17, 835, 10.7314/APJCP.2016.17.2.835
Rapaport, 2008, Classification of arrayCGH data using fused SVM, Bioinformatics, 24, i375, 10.1093/bioinformatics/btn188
Reed, 2012, Shrinking the metabolic solution space using experimental datasets, PLoS Comput Biol, 8, 10.1371/journal.pcbi.1002662
Salvador, 2017, Lipid metabolism and lung cancer, Critical Reviews in Oncology/Hematology, 112, 31, 10.1016/j.critrevonc.2017.02.001
Schmidt, 2013, GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data, Bioinformatics, 29, 2900, 10.1093/bioinformatics/btt493
Stepulak, 2014, Glutamate and its receptors in cancer, Journal of neural transmission, 121, 933, 10.1007/s00702-014-1182-6
Tian, 2020, Leukotrienes in Tumor-Associated Inflammation, Frontiers in Pharmacology, 11, 1289, 10.3389/fphar.2020.01289
Tyanova, 2016, Proteomic maps of breast cancer subtypes, Nature communications, 7, 1, 10.1038/ncomms10259
Vural, 2016, Classification of breast cancer patients using somatic mutation profiles and machine learning approaches, BMC systems biology, 10, 263
Warburg, 1956, On the origin of cancer cells, Science, 123, 309, 10.1126/science.123.3191.309
Wikoff, 2015, Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma, Cancer prevention research, 8, 410, 10.1158/1940-6207.CAPR-14-0329
Xie, 2021, Early lung cancer diagnostic biomarker discovery by machine learning methods, Translational oncology, 14, 10.1016/j.tranon.2020.100907
Yang, 2017, Classification based on feature extraction for hepatocellular carcinoma diagnosis using high-throughput dna methylation sequencing data, Procedia Computer Science, 107, 412, 10.1016/j.procs.2017.03.130
Yu, 2012, ‘clusterProfiler: an R package for comparing biological themes among gene clusters, Omics: a journal of integrative biology, 16, 284, 10.1089/omi.2011.0118
Zur, 2010, ‘iMAT: an integrative metabolic analysis tool, Bioinformatics, 26, 3140, 10.1093/bioinformatics/btq602