Publikationer av Cheng Zhang
Refereegranskade
Artiklar
[1]
W. Kim et al., "Characterization of an in vitro steatosis model simulating activated de novo lipogenesis in MAFLD patients," iScience, vol. 26, no. 10, 2023.
[2]
B. Yulug et al., "Combined metabolic activators improve cognitive functions in Alzheimer's disease patients : a randomised, double-blinded, placebo-controlled phase-II trial," Translational Neurodegeneration, vol. 12, no. 1, 2023.
[3]
H. Turkez et al., "Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases," Life Sciences, vol. 314, s. 121325, 2023.
[4]
O. K. Graves et al., "Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma," Biomedicine and Pharmacotherapy, vol. 161, 2023.
[5]
A. Bayraktar et al., "Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients," Journal of Translational Medicine, vol. 21, no. 1, 2023.
[6]
U. M. Battisti et al., "Ellagic Acid and Its Metabolites as Potent and Selective Allosteric Inhibitors of Liver Pyruvate Kinase," Nutrients, vol. 15, no. 3, s. 577, 2023.
[7]
H. Yang et al., "Longitudinal metabolomics analysis reveals the acute effect of cysteine and NAC included in the combined metabolic activators," Free Radical Biology & Medicine, vol. 204, s. 347-358, 2023.
[8]
X. Liao et al., "Open MoA : revealing the mechanism of action (MoA) based on network topology and hierarchy," Bioinformatics, vol. 39, no. 11, 2023.
[9]
H. Jin et al., "Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation," Nature Communications, vol. 14, no. 1, s. 5417, 2023.
[10]
X. Li et al., "The acute effect of different NAD+ precursors included in the combined metabolic activators," Free Radical Biology & Medicine, vol. 205, s. 77-89, 2023.
[11]
L. Voland et al., "Tissue pleiotropic effect of biotin and prebiotic supplementation in established obesity," American Journal of Physiology. Endocrinology and Metabolism, vol. 325, no. 4, s. E390-E405, 2023.
[12]
M. Yuan et al., "A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma," Cancers, vol. 14, no. 6, 2022.
[13]
C. Zhang et al., "Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning," EBioMedicine, vol. 83, 2022.
[14]
M. Karlsson et al., "Genome-wide annotation of protein-coding genes in pig," BMC Biology, vol. 20, no. 1, 2022.
[15]
S. Smati et al., "Integrative study of diet-induced mouse models of NAFLD identifies PPARα as a sexually dimorphic drug target," Gut, vol. 71, no. 4, s. 807-821, 2022.
[16]
A. T. Ambikan et al., "Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity," Cell Systems, vol. 13, no. 8, s. 665-681.e4, 2022.
[17]
M. Zeybel et al., "Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis," Advanced Science, vol. 9, no. 11, s. 2104373, 2022.
[18]
X. Li et al., "Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach," EBioMedicine, vol. 78, s. 103963, 2022.
[19]
H. Yang et al., "A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic liver disease," iScience, vol. 24, no. 11, 2021.
[20]
M. Karlsson et al., "A single-cell type transcriptomics map of human tissues," Science Advances, vol. 7, no. 31, 2021.
[21]
Y. Yang et al., "Advances in the Relationships Between Cow's Milk Protein Allergy and Gut Microbiota in Infants," Frontiers in Microbiology, vol. 12, 2021.
[22]
Ö. Altay et al., "Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19," Advanced Science, vol. 8, no. 17, 2021.
[23]
H. Yang et al., "Combined Metabolic Activators Decrease Liver Steatosis by Activating Mitochondrial Metabolism in Hamsters Fed with a High-Fat Diet," Biomedicines, vol. 9, no. 10, 2021.
[24]
M. Zeybel et al., "Combined metabolic activators therapy ameliorates liver fat in nonalcoholic fatty liver disease patients," Molecular Systems Biology, vol. 17, no. 10, 2021.
[25]
X. Li et al., "Discovery of Functional Alternatively Spliced PKM Transcripts in Human Cancers," Cancers, vol. 13, no. 2, 2021.
[26]
J. R. Bosley et al., "Informing Pharmacokinetic Models With Physiological Data : Oral Population Modeling of L-Serine in Humans," Frontiers in Pharmacology, vol. 12, 2021.
[27]
M. Arif et al., "Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myocardial infarction," eLIFE, vol. 10, 2021.
[28]
L.-J. Zhao et al., "Lysine demethylase LSD1 delivered via small extracellular vesicles promotes gastric cancer cell stemness," EMBO Reports, vol. 22, no. 8, 2021.
[29]
A. Bayraktar et al., "Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis," International Journal of Molecular Sciences, vol. 22, no. 21, 2021.
[30]
D. Mahdessian et al., "Spatiotemporal dissection of the cell cycle with single-cell proteogenomics," Nature, vol. 590, no. 7847, 2021.
[31]
X. Li et al., "Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning," iScience, vol. 24, no. 7, 2021.
[32]
S. Lam et al., "Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases," Biomedicines, vol. 9, no. 10, 2021.
[33]
A. Kaynar et al., "Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme," International Journal of Molecular Sciences, vol. 22, no. 24, s. 13213, 2021.
[34]
M. Arif et al., "iNetModels 2.0 : an interactive visualization and database of multi-omics data.," Nucleic Acids Research, vol. 49, no. W1, s. W271-W276, 2021.
[35]
S. Lam et al., "A systems biology approach for studying neurodegenerative diseases," Drug Discovery Today, vol. 25, no. 7, s. 1146-1159, 2020.
[36]
E. Sjöstedt et al., "An atlas of the protein-coding genes in the human, pig, and mouse brain," Science, vol. 367, no. 6482, s. 1090-+, 2020.
[37]
E. C. Sayitoglu et al., "Boosting Natural Killer Cell-Mediated Targeting of Sarcoma Through DNAM-1 and NKG2D," Frontiers in Immunology, vol. 11, 2020.
[38]
X. Li et al., "Classification of clear cell renal cell carcinoma based on PKM alternative splicing," Heliyon, vol. 6, no. 2, 2020.
[39]
C. Lieven et al., "Correction: MEMOTE for standardized genome-scale metabolic model testing (vol 38, pg 272, 2020)," Nature Biotechnology, vol. 38, no. 4, s. 504-504, 2020.
[40]
I. Larsson et al., "Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development," Frontiers in Genetics, vol. 11, 2020.
[41]
T. Abdellah et al., "Integration of molecular profiles in a longitudinal wellness profiling cohort," Nature Communications, vol. 11, no. 1, 2020.
[42]
C. Lieven et al., "MEMOTE for standardized genome-scale metabolic model testing," Nature Biotechnology, vol. 38, no. 3, s. 272-276, 2020.
[43]
C. Zhang et al., "The acute effect of metabolic cofactor supplementation : a potential therapeutic strategy against non-alc33oholic fatty liver disease," Molecular Systems Biology, vol. 16, no. 4, 2020.
[44]
Y. Sun et al., "A fuzzy programming method for modeling demand uncertainty in the capacitated road-rail multimodal routing problem with time windows," Symmetry, vol. 11, no. 1, 2019.
[45]
M. Uhlén et al., "A genome-wide transcriptomic analysis of protein-coding genes in human blood cells," Science, vol. 366, no. 6472, s. 1471-+, 2019.
[46]
C. Pineau et al., "Cell Type-Specific Expression of Testis Elevated Genes Based on Transcriptomics and Antibody-Based Proteomics," Journal of Proteome Research, vol. 18, no. 12, s. 4215-4230, 2019.
[47]
R. Benfeitas et al., "Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis," EBioMedicine, vol. 40, s. 471-487, 2019.
[48]
S. Lundgren et al., "Discovery of KIRREL as a biomarker for prognostic stratification of patients within melanoma," Biomarker Research, 2019.
[49]
B. Turanli et al., "Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning," EBioMedicine, vol. 42, s. 386-396, 2019.
[50]
C. Zhang et al., "Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling," Frontiers in Oncology, vol. 9, 2019.
[51]
M. C. Svensson et al., "Expression of PD-L1 and PD-1 in Chemoradiotherapy-Naïve Esophageal and Gastric Adenocarcinoma: Relationship With Mismatch Repair Status and Survival," Frontiers in Oncology, 2019.
[52]
C. Cadenas et al., "LIPG-promoted lipid storage mediates adaptation to oxidative stress in breast cancer," International Journal of Cancer, vol. 145, no. 4, s. 901-915, 2019.
[53]
M. J. Harms et al., "Mature Human White Adipocytes Cultured under Membranes Maintain Identity, Function, and Can Transdifferentiate into Brown-like Adipocytes," Cell Reports, 2019.
[54]
Z. Liu et al., "Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function," Metabolic engineering, 2019.
[55]
A. Mardinoglu et al., "An Integrated Understanding of the Rapid Metabolic Benefits of a Carbohydrate-Restricted Diet on Hepatic Steatosis in Humans," Cell Metabolism, vol. 27, no. 3, s. 559-571.e1-e5, 2018.
[56]
C. Zhang et al., "ESS : A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling," Frontiers in Physiology, vol. 9, 2018.
[57]
B. D. Piening et al., "Integrative Personal Omics Profiles during Periods of Weight Gain and Loss," Cell Systems, 2018.
[58]
G. Bidkhori et al., "Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes," Proceedings of the National Academy of Sciences of the United States of America, 2018.
[59]
A. Olin et al., "Stereotypic Immune System Development in Newborn Children," Cell, vol. 174, no. 5, s. 1277-+, 2018.
[60]
F. Danielsson et al., "Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia," Oncotarget, vol. 9, no. 28, s. 19730-19744, 2018.
[61]
D. Rosario et al., "Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling," Frontiers in Physiology, vol. 9, 2018.
[62]
M. Uhlén et al., "A pathology atlas of the human cancer transcriptome," Science, vol. 357, no. 6352, s. 660-+, 2017.
[63]
[64]
B. J. Sanchez et al., "Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints," Molecular Systems Biology, vol. 13, no. 8, 2017.
[65]
S. Lee et al., "Network analyses identify liver-specific targets for treating liver diseases," Molecular Systems Biology, 2017.
[66]
A. Mardinoglu et al., "Personal model-assisted identification of NAD(+) and glutathione metabolism as intervention target in NAFLD," Molecular Systems Biology, vol. 13, no. 3, 2017.
[67]
S. Wei et al., "Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol," Bioresources and Bioprocessing, vol. 4, no. 1, 2017.
[68]
S. Lee et al., "TCSBN: a database of tissue and cancer specific biological networks," Nucleic Acids Research, vol. 46, no. D1, s. D595-D600, 2017.
[69]
C. Zhang och Q. Hua, "Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine : Application of GEMs," Frontiers in Physiology, vol. 6, no. January, 2016.
[70]
S. Lee et al., "Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis," Nucleic Acids Research, vol. 44, no. 12, s. 5529-5539, 2016.
[71]
D. Gu et al., "IdealKnock: A framework for efficiently identifying knockout strategiesleading to targeted overproduction," Computational biology and chemistry (Print), 2016.
[72]
X. Jian et al., "In silico identification of gene amplification targets based on analysisof production and growth coupling," Biosystems (Amsterdam. Print), 2016.
[73]
X. Jian et al., "In silico profiling of cell growthand succinate production in Escherichia coli NZN111," Bioresources and Bioprocessing, 2016.
[74]
S. Lee et al., "Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance," Cell Metabolism, vol. 24, no. 1, s. 172-184, 2016.
[75]
C. Zhang et al., "Investigating the Combinatory Effects of Biological Networks on Gene Co-expression," Frontiers in Physiology, vol. 7, 2016.
[76]
D. Gu et al., "Reframed genome-scale metabolic model to facilitate genetic design and integration with expression data," IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2016.
[77]
C. Zhang, "Logical transformation of genome-scale metabolic models for gene level applications and analysis," Bioinformatics, vol. 31, no. 14, s. 2324-2331, 2015.
[78]
A. Mardinoglu et al., "The gut microbiota modulates host amino acid and glutathione metabolism in mice," Molecular Systems Biology, vol. 11, no. 10, 2015.
Icke refereegranskade
Artiklar
[79]
H. Yang et al., "Revisiting the role of serine metabolism in hepatic lipogenesis," Nature Metabolism, vol. 5, no. 5, s. 760-761, 2023.
[80]
Z. Liu et al., "Editorial : Systems biology approach for the mechanisms underlying chronic liver disease," Frontiers in Medicine, vol. 9, 2022.
[81]
Y. Wei et al., "Editorial : Application of systems biology in molecular characterization and diagnosis of cancer, Volume II," Frontiers in Molecular Biosciences, vol. 9, 2022.
[82]
C. Zhang et al., "Editorial : Application of Systems Biology in Molecular Characterization and Diagnosis of Cancer," Frontiers in Molecular Biosciences, vol. 8, 2021.
Övriga
[83]
M. Karlsson et al., "Genome-wide single cell annotation of the human protein-coding genes," (Manuskript).
[84]
[85]
D. Mahdessian et al., "Spatiotemporal dissection of the cell cycle regulated human proteome," (Manuskript).
Senaste synkning med DiVA:
2024-04-21 04:21:22