Metabolomics meets systems immunology
暂无分享,去创建一个
[1] P. Carmeliet,et al. Analyzing cell-type-specific dynamics of metabolism in kidney repair , 2022, Nature Metabolism.
[2] E. Carrilho,et al. 1H qNMR-Based Metabolomics Discrimination of Covid-19 Severity , 2022, Journal of proteome research.
[3] A. Conesa,et al. PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases , 2022, Nucleic Acids Res..
[4] Kuerbannaimu Kaheman,et al. Lactobacillus acidophilus and HKL Suspension Alleviates Ulcerative Colitis in Rats by Regulating Gut Microbiota, Suppressing TLR9, and Promoting Metabolism , 2022, Frontiers in Pharmacology.
[5] H. Steen,et al. Bacille Calmette–Guérin vaccine reprograms human neonatal lipid metabolism in vivo and in vitro , 2022, Cell reports.
[6] Xin Bing,et al. H1N1 Influenza Virus-Infected Nasal Mucosal Epithelial Progenitor Cells Promote Dendritic Cell Recruitment and Maturation , 2022, Frontiers in Immunology.
[7] Wei Li,et al. The Host CYP1A1-Microbiota Metabolic Axis Promotes Gut Barrier Disruption in Methicillin-Resistant Staphylococcus aureus-Induced Abdominal Sepsis , 2022, Frontiers in Microbiology.
[8] Jun Liu,et al. Novel potential metabolic biomarker panel for early detection of severe COVID-19 using full-spectrum metabolome and whole-transcriptome analyses , 2022, Signal Transduction and Targeted Therapy.
[9] Alese E. Halvorson,et al. Iron deficiency linked to altered bile acid metabolism promotes Helicobacter pylori–induced inflammation–driven gastric carcinogenesis , 2022, The Journal of clinical investigation.
[10] Xin Chen,et al. Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis , 2022, Journal of Translational Medicine.
[11] Druszczynska Magdalena,et al. Targeted metabolomics analysis of serum and Mycobacterium tuberculosis antigen-stimulated blood cultures of pediatric patients with active and latent tuberculosis , 2022, Scientific Reports.
[12] S. Yelamanchi,et al. Metabolite Dysregulation by Pranlukast in Mycobacterium tuberculosis , 2022, Molecules.
[13] Y. Zhu,et al. The Landscape of Featured Metabolism-Related Genes and Imbalanced Immune Cell Subsets in Sepsis , 2022, Frontiers in Genetics.
[14] R. Black,et al. National, regional, and global causes of mortality in 5–19-year-olds from 2000 to 2019: a systematic analysis , 2022, The Lancet. Global health.
[15] C. Barbas,et al. Metabolic Snapshot of Plasma Samples Reveals New Pathways Implicated in SARS-CoV-2 Pathogenesis. , 2022, Journal of proteome research.
[16] N. Yosef,et al. Systems-based approaches to study immunometabolism , 2022, Cellular & Molecular Immunology.
[17] Reed J. D. Sorensen,et al. Pandemic preparedness and COVID-19: an exploratory analysis of infection and fatality rates, and contextual factors associated with preparedness in 177 countries, from Jan 1, 2020, to Sept 30, 2021 , 2022, The Lancet.
[18] Mark S. Anderson,et al. Human genetic and immunological determinants of critical COVID-19 pneumonia , 2022, Nature.
[19] Jiangjiang Zhu,et al. Adaptive Metabolism of Staphylococcus aureus Revealed by Untargeted Metabolomics. , 2022, Journal of proteome research.
[20] Gustavo Daniel Vega Magdaleno,et al. Trans cohort metabolic reprogramming towards glutaminolysis in long-term successfully treated HIV-infection , 2022, Communications Biology.
[21] W. Zhang,et al. Integrated hepatic single-cell RNA sequencing and untargeted metabolomics reveals the immune and metabolic modulation of Qing-Fei-Pai-Du decoction in mice with coronavirus-induced pneumonia , 2022, Phytomedicine.
[22] A. Gruca,et al. MAINE: a web tool for multi-omics feature selection and rule-based data exploration , 2021, Bioinform..
[23] Qian Liang,et al. Metabolomics profile in acute respiratory distress syndrome by nuclear magnetic resonance spectroscopy in patients with community-acquired pneumonia , 2021, Respiratory Research.
[24] Xilong Deng,et al. Metabolomic analyses reveal new stage-specific features of COVID-19 , 2021, European Respiratory Journal.
[25] OUP accepted manuscript , 2022, Nucleic Acids Research.
[26] Feng Zhu,et al. Optimization of metabolomic data processing using NOREVA , 2021, Nature Protocols.
[27] K. Klavins,et al. Amino Acid Metabolism is Significantly Altered at the Time of Admission in Hospital for Severe COVID-19 Patients: Findings from Longitudinal Targeted Metabolomics Analysis , 2021, Microbiology spectrum.
[28] Luang Xu,et al. Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19 , 2021, Cell Reports.
[29] B. Haynes,et al. Preexisting memory CD4+ T cells contribute to the primary response in an HIV-1 vaccine trial , 2021, The Journal of clinical investigation.
[30] David B. Blumenthal,et al. Network medicine for disease module identification and drug repurposing with the NeDRex platform , 2021, Nature Communications.
[31] A. del Sol,et al. Fostering experimental and computational synergy to modulate hyperinflammation , 2021, Trends in immunology.
[32] C. Conrad,et al. Untimely TGFβ responses in COVID-19 limit antiviral functions of NK cells , 2021, Nature.
[33] J. Baumbach,et al. MoSBi: Automated signature mining for molecular stratification and subtyping , 2021, bioRxiv.
[34] A. Sönnerborg,et al. Metabolic Perturbation Associated With COVID-19 Disease Severity and SARS-CoV-2 Replication , 2021, Molecular & Cellular Proteomics.
[35] G. Siuzdak,et al. Metabolomics activity screening of T cell–induced colitis reveals anti-inflammatory metabolites , 2021, Science Signaling.
[36] Michael J. Ryan,et al. Considerations in boosting COVID-19 vaccine immune responses , 2021, The Lancet.
[37] Mark M. Davis,et al. Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19 , 2021, Nature Biotechnology.
[38] Yingying Ding,et al. Comprehensive metabolomics profiling reveals common metabolic alterations underlying the four major non-communicable diseases in treated HIV infection , 2021, EBioMedicine.
[39] Sherlly Lim,et al. Comparison between non-pulmonary and pulmonary immune responses in a HIV decedent who succumbed to COVID-19 , 2021, Gut.
[40] E. Ma,et al. Interrogating in vivo T-cell metabolism in mice using stable isotope labeling metabolomics and rapid cell sorting , 2021, Nature Protocols.
[41] R. Xavier,et al. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease , 2021, Genome biology.
[42] A. Sandberg,et al. Associations of maternal and infant metabolomes with immune maturation and allergy development at 12 months in the Swedish NICE-cohort , 2021, Scientific Reports.
[43] Y. Idaghdour,et al. Metabolome modulation of the host adaptive immunity in human malaria , 2021, Nature Metabolism.
[44] N. Lall,et al. Elucidating the Antimycobacterial Mechanism of Action of Decoquinate Derivative RMB041 Using Metabolomics , 2021, Antibiotics.
[45] J. Asara,et al. Pre-operative exercise therapy triggers anti-inflammatory trained immunity of Kupffer cells through metabolic reprogramming , 2021, Nature Metabolism.
[46] Xia Yang,et al. Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics , 2021, Nucleic Acids Res..
[47] M. Heikenwalder,et al. SpaceM reveals metabolic states of single cells , 2021, Nature Methods.
[48] L. Wolska,et al. Determination of amino acids in human biological fluids by high-performance liquid chromatography: critical review , 2021, Amino Acids.
[49] S. Galea,et al. A global public health convention for the 21st century , 2021, The Lancet Public Health.
[50] Mark M. Davis,et al. Systems Immunology: Revealing Influenza Immunological Imprint , 2021, Viruses.
[51] Ó. Pozo,et al. Metabolic Signatures Associated with Severity in Hospitalized COVID-19 Patients , 2021, International journal of molecular sciences.
[52] N. Lall,et al. Elucidating the Antimycobacterial Mechanism of Action of Ciprofloxacin Using Metabolomics , 2021, Microorganisms.
[53] R. Mandal,et al. Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 , 2021, Scientific Reports.
[54] J. Ison,et al. APE in the Wild: Automated Exploration of Proteomics Workflows in the bio.tools Registry , 2021, Journal of proteome research.
[55] Shao Li,et al. Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications , 2021, Nature communications.
[56] Y. Bao,et al. Hyocholic acid species as novel biomarkers for metabolic disorders , 2021, Nature Communications.
[57] M. Cookson,et al. CoExp: A Web Tool for the Exploitation of Co-expression Networks , 2021, Frontiers in Genetics.
[58] B. Amer,et al. Omics-Driven Biotechnology for Industrial Applications , 2021, Frontiers in Bioengineering and Biotechnology.
[59] J. Schultze,et al. COVID-19 and the human innate immune system , 2021, Cell.
[60] M. Netea,et al. Resolving trained immunity with systems biology , 2021, European journal of immunology.
[61] Sarah Stowell. Datasets , 2021, Algebraic Analysis of Social Networks.
[62] Chun Jimmie Ye,et al. Global absence and targeting of protective immune states in severe COVID-19 , 2021, Nature.
[63] J. Boccard,et al. Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections , 2021, Metabolites.
[64] Samuel Chaffron,et al. MiBiOmics: an interactive web application for multi-omics data exploration and integration , 2021, BMC Bioinformatics.
[65] D. Kell,et al. Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome , 2020, Metabolomics.
[66] A. Telenti,et al. Transfer transcriptomic signatures for infectious diseases , 2020, Proceedings of the National Academy of Sciences.
[67] Hyungwon Choi,et al. multiSLIDE: a web server for exploring connected elements of biological pathways in multi-omics data , 2019, bioRxiv.
[68] B. Sathian,et al. A contemporary insight of metabolomics approach for COVID-19: Potential for novel therapeutic and diagnostic targets , 2020, Nepal Journal of Epidemiology.
[69] M. Shokhirev,et al. SUMMER, a shiny utility for metabolomics and multiomics exploratory research , 2020, Metabolomics.
[70] L. Joosten,et al. Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome , 2020, Journal of Translational Medicine.
[71] L. Wood,et al. Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease , 2020, Nature Communications.
[72] Li Bo,et al. MMEASE: Online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis. , 2020, Journal of proteomics.
[73] William T. Hu,et al. Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19 , 2020, Nature Immunology.
[74] Quan Liu,et al. IP4M: an integrated platform for mass spectrometry-based metabolomics data mining , 2020, BMC Bioinformatics.
[75] Maxim N. Artyomov,et al. Immunometabolism in the Single-Cell Era. , 2020, Cell metabolism.
[76] Mark M. Davis,et al. The science and medicine of human immunology , 2020, Science.
[77] M. Medina. Metabolic Reprogramming is a Hallmark of Metabolism Itself , 2020, BioEssays : news and reviews in molecular, cellular and developmental biology.
[78] P. Dorrestein,et al. Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Signatures , 2020, Cell.
[79] Y. Mineur,et al. Origin and Function of Stress-Induced IL-6 in Murine Models , 2020, Cell.
[80] Z. Cai,et al. Large-scale targeted metabolomics method for metabolite profiling of human samples. , 2020, Analytica chimica acta.
[81] Madeleine K. D. Scott,et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans , 2020, Science.
[82] I. Sereti,et al. Immunometabolism and HIV-1 pathogenesis: food for thought , 2020, Nature Reviews Immunology.
[83] Mark M. Davis. Systems immunology , 2020, Current Opinion in Immunology.
[84] J. Chan,et al. Metabolic Profiling Reveals Significant Perturbations of Intracellular Glucose Homeostasis in Enterovirus-Infected Cells , 2020, Metabolites.
[85] T. Alexandrov. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. , 2020, Annual review of biomedical data science.
[86] Gek Huey Chua,et al. Omics-Driven Systems Interrogation of Metabolic Dysregulation in COVID-19 Pathogenesis , 2020, Cell Metabolism.
[87] J. E. Slovak,et al. Pharmacometabolomics with a combination of PLS-DA and random forest algorithm analyses reveal meloxicam alters feline plasma metabolite profiles. , 2020, Journal of veterinary pharmacology and therapeutics.
[88] E. Hod,et al. COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status. , 2020, JCI insight.
[89] J. Adamski,et al. Inflammatory macrophage memory in NSAID-exacerbated respiratory disease. , 2020, The Journal of allergy and clinical immunology.
[90] M. Ghosh,et al. Single Cell Metabolomics: A Future Tool to Unmask Cellular Heterogeneity and Virus-Host Interaction in Context of Emerging Viral Diseases , 2020, Frontiers in Microbiology.
[91] J. D. de Fijter,et al. Urinary metabolites associate with the rate of kidney function decline in patients with autosomal dominant polycystic kidney disease , 2020, PloS one.
[92] H. Spaink,et al. Analyzing the impact of Mycobacterium tuberculosis infection on primary human macrophages by combined exploratory and targeted metabolomics , 2020, Scientific Reports.
[93] Huanhuan Gao,et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera , 2020, Cell.
[94] T. Cowan,et al. Metabolic profiling by reversed-phase/ion-exchange mass spectrometry. , 2020, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.
[95] Y. Kotliarov,et al. Multi-modal immune phenotyping of maternal peripheral blood in normal human pregnancy. , 2020, JCI insight.
[96] Sang-Nae Cho,et al. Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach , 2020, Scientific Reports.
[97] Robyn M. Kaake,et al. A systems approach to infectious disease , 2020, Nature Reviews Genetics.
[98] Regina Joice Cordy. Mining the Human Host Metabolome Toward an Improved Understanding of Malaria Transmission , 2020, Frontiers in Microbiology.
[99] Chi Wang,et al. Improved workflow for mass spectrometry–based metabolomics analysis of the heart , 2020, The Journal of Biological Chemistry.
[100] Shuzhao Li,et al. Pathway Analysis for Targeted and Untargeted Metabolomics. , 2020, Methods in molecular biology.
[101] J. Ayres,et al. Metabolic Adaptations to Infections at the Organismal Level. , 2020, Trends in immunology.
[102] Andreas Handel,et al. A software package for immunologists to learn simulation modeling , 2020, BMC immunology.
[103] V. V. Ivanov,et al. Plasma metabolomics of the time resolved response to Opisthorchis felineus infection in an animal model (golden hamster, Mesocricetus auratus) , 2020, PLoS neglected tropical diseases.
[104] Bindesh Shrestha. Single-Cell Metabolomics by Mass Spectrometry. , 2020, Methods in molecular biology.
[105] David Rojo,et al. RECENT DEVELOPMENTS ALONG THE ANALYTICAL PROCESS FOR METABOLOMICS WORKFLOWS. , 2020, Analytical chemistry.
[106] J. Ayres,et al. Immunometabolism of infections , 2019, Nature Reviews Immunology.
[107] Sean C. Bendall,et al. Immune monitoring using mass cytometry and related high-dimensional imaging approaches , 2019, Nature Reviews Rheumatology.
[108] Andreas Handel,et al. Simulation modelling for immunologists , 2019, Nature Reviews Immunology.
[109] Pingli Wei,et al. Metandem: An online software tool for mass spectrometry-based isobaric labeling metabolomics. , 2019, Analytica chimica acta.
[110] J. Kalinowski,et al. Physiology and Transcriptional Analysis of (p)ppGpp-Related Regulatory Effects in Corynebacterium glutamicum , 2019, Front. Microbiol..
[111] Vladimir Shulaev,et al. Metabolomics technology and bioinformatics for precision medicine , 2019, Briefings Bioinform..
[112] B. Colsch,et al. Blood metabolomics uncovers inflammation-associated mitochondrial dysfunction as a potential mechanism underlying ACLF. , 2019, Journal of hepatology.
[113] Takla Griss,et al. Metabolic Profiling Using Stable Isotope Tracing Reveals Distinct Patterns of Glucose Utilization by Physiologically Activated CD8+ T Cells. , 2019, Immunity.
[114] Do Yup Lee,et al. Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE , 2019, Scientific Reports.
[115] L. Weinberger,et al. Attacking Latent HIV with convertibleCAR-T Cells, a Highly Adaptable Killing Platform , 2019, Cell.
[116] Miguel Rocha,et al. WebSpecmine: A Website for Metabolomics Data Analysis and Mining , 2019, Metabolites.
[117] D. Wishart. Metabolomics for Investigating Physiological and Pathophysiological Processes. , 2019, Physiological reviews.
[118] V. Viswanathan,et al. Plasma Metabolic Signature and Abnormalities in HIV-Infected Individuals on Long-Term Successful Antiretroviral Therapy , 2019, Metabolites.
[119] S. Tans,et al. Deciphering metabolic heterogeneity by single-cell analysis. , 2019, Analytical chemistry.
[120] Jeremy R Everett,et al. A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis. , 2019, Trends in pharmacological sciences.
[121] Shisheng Wang,et al. pseudoQC: A Regression‐Based Simulation Software for Correction and Normalization of Complex Metabolomics and Proteomics Datasets , 2019, Proteomics.
[122] Andres Metspalu,et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals , 2019, Nature Communications.
[123] Zheng-Jiang Zhu,et al. MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery , 2019, Bioinform..
[124] Yie Hou Lee,et al. Metabolic perturbations and cellular stress underpin susceptibility to symptomatic live-attenuated yellow fever infection , 2019, Nature Medicine.
[125] Hyungwon Choi,et al. iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery , 2019, npj Systems Biology and Applications.
[126] Pingli Wei,et al. Urinary Metabolomic and Proteomic Analyses in a Mouse Model of Prostatic Inflammation. , 2019, Urine.
[127] Markus M. Rinschen,et al. Identification of bioactive metabolites using activity metabolomics , 2019, Nature Reviews Molecular Cell Biology.
[128] Subha Madhavan,et al. Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities , 2019, Cell.
[129] M. Jacob,et al. Metabolomics toward personalized medicine. , 2019, Mass spectrometry reviews.
[130] Natapol Pornputtapong,et al. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data , 2019, Computational and structural biotechnology journal.
[131] D. Havlir,et al. A pilot metabolomics study of tuberculosis immune reconstitution inflammatory syndrome , 2019, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.
[132] P. Khatri,et al. Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures , 2019, PLoS medicine.
[133] E. Rodríguez-Gallego,et al. Immunometabolism is a key factor for the persistent spontaneous elite control of HIV-1 infection , 2019, EBioMedicine.
[134] J. Banchereau,et al. Author Correction: Progression of whole-blood transcriptional signatures from interferon-induced to neutrophil-associated patterns in severe influenza , 2019, Nature Immunology.
[135] R. Trengove,et al. Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases , 2019, Metabolites.
[136] J. Asara,et al. Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC–MS/MS , 2019, Nature Protocols.
[137] E. Decroly,et al. FTSJ3 is an RNA 2′-O-methyltransferase recruited by HIV to avoid innate immune sensing , 2019, Nature.
[138] Yuri Motorin,et al. FTSJ3 is an RNA 2′-O-methyltransferase recruited by HIV to avoid innate immune sensing , 2019, Nature.
[139] Fredrik Levander,et al. NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis. , 2018, Journal of proteome research.
[140] Bruno Agard,et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy , 2018, Bioinform..
[141] Michel R. Klein,et al. Metabolite changes in blood predict the onset of tuberculosis , 2018, Nature Communications.
[142] M. Giera,et al. KIMBLE: A versatile visual NMR metabolomics workbench in KNIME. , 2018, Analytica chimica acta.
[143] Ruiping Zhang,et al. Development of simultaneous targeted metabolite quantification and untargeted metabolomics strategy using dual-column liquid chromatography coupled with tandem mass spectrometry. , 2018, Analytica chimica acta.
[144] T. Hankemeier,et al. Persistent metabolic changes in HIV-infected patients during the first year of combination antiretroviral therapy , 2018, Scientific Reports.
[145] W. Liu,et al. Arginine deficiency is involved in thrombocytopenia and immunosuppression in severe fever with thrombocytopenia syndrome , 2018, Science Translational Medicine.
[146] Alejandra N. González-Beltrán,et al. PhenoMeNal: processing and analysis of metabolomics data in the cloud , 2018, bioRxiv.
[147] Yicheng Wang,et al. 1H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers. , 2018, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.
[148] Jingqiu Cheng,et al. MetaboGroup S: A Group Entropy-Based Web Platform for Evaluating Normalization Methods in Blood Metabolomics Data from Maintenance Hemodialysis Patients. , 2018, Analytical chemistry.
[149] Weiqi Wang,et al. IgG3 regulates tissue-like memory B cells in HIV-infected individuals , 2018, Nature Immunology.
[150] Xing-Quan Zhu,et al. Hepatic Metabolomics Investigation in Acute and Chronic Murine Toxoplasmosis , 2018, Front. Cell. Infect. Microbiol..
[151] David S. Wishart,et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis , 2018, Nucleic Acids Res..
[152] J. Rabinowitz,et al. Metabolomics and Isotope Tracing , 2018, Cell.
[153] Ludovic Cottret,et al. MetExplore: collaborative edition and exploration of metabolic networks , 2018, Nucleic Acids Res..
[154] E. Pearce,et al. Unraveling the Complex Interplay Between T Cell Metabolism and Function. , 2018, Annual review of immunology.
[155] Nir Hacohen,et al. Systems Immunology: Learning the Rules of the Immune System. , 2018, Annual review of immunology.
[156] Martin Jaeger,et al. Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses , 2018, Nature Immunology.
[157] J. Marioni,et al. Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets , 2018, Molecular systems biology.
[158] Maxim N. Artyomov,et al. Electrophilic properties of itaconate and derivatives regulate the IκBζ–ATF3 inflammatory axis , 2018, Nature.
[159] Thomas Gross,et al. Toward Reproducible Results from Targeted Metabolomic Studies: Perspectives for Data Pre-processing and a Basis for Analytic Pipeline Development. , 2018, Current topics in medicinal chemistry.
[160] Edward T Chouchani,et al. Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1 , 2018, Nature.
[161] A. D. De Livera,et al. NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data , 2018, Metabolomics : Official journal of the Metabolomic Society.
[162] Tao Huan,et al. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online , 2018, Nature Protocols.
[163] Christoph Steinbeck,et al. nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data. , 2018, Analytical chemistry.
[164] David S. Wishart,et al. HMDB 4.0: the human metabolome database for 2018 , 2017, Nucleic Acids Res..
[165] Jasper Engel,et al. Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients , 2018, Journal of Inherited Metabolic Disease.
[166] M. Akmatov,et al. Mass-spectrometric profiling of cerebrospinal fluid reveals metabolite biomarkers for CNS involvement in varicella zoster virus reactivation , 2018, Journal of Neuroinflammation.
[167] F. Priego-Capote,et al. MetaboQC: A tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls. , 2017, Talanta.
[168] Nathalie Villa-Vialaneix,et al. Unsupervised multiple kernel learning for heterogeneous data integration , 2017, bioRxiv.
[169] Jamey D. Young,et al. Lactate Metabolism in Human Lung Tumors , 2017, Cell.
[170] G. Ermentrout,et al. Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity , 2017, Journal of Virology.
[171] Wu Zhu,et al. Lipidomics profiling reveals the role of glycerophospholipid metabolism in psoriasis , 2017, GigaScience.
[172] Yann Guitton,et al. Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. , 2017, The international journal of biochemistry & cell biology.
[173] O. Fiehn,et al. Metabolite Measurement: Pitfalls to Avoid and Practices to Follow. , 2017, Annual Review of Biochemistry.
[174] Ting-Li Han,et al. Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy , 2017, F1000Research.
[175] Mark M. Davis,et al. Systems immunology: just getting started , 2017, Nature Immunology.
[176] Kim-Anh Lê Cao,et al. mixOmics: An R package for ‘omics feature selection and multiple data integration , 2017, bioRxiv.
[177] Michael D. Buck,et al. Metabolic Instruction of Immunity , 2017, Cell.
[178] D. Viemann,et al. S100-alarmin-induced innate immune programming protects newborn infants from sepsis , 2017, Nature Immunology.
[179] Siqi Liu,et al. metaX: a flexible and comprehensive software for processing metabolomics data , 2017, BMC Bioinformatics.
[180] Pieter C Dorrestein,et al. From single cells to our planet-recent advances in using mass spectrometry for spatially resolved metabolomics. , 2017, Current opinion in chemical biology.
[181] Junlei Chang,et al. Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states , 2017, Nature Medicine.
[182] C. Deborde,et al. NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics , 2016, Metabolomics.
[183] I. Losito,et al. Hydrophilic interaction and reversed phase mixed-mode liquid chromatography coupled to high resolution tandem mass spectrometry for polar lipids analysis. , 2016, Journal of chromatography. A.
[184] Liam G. Fearnley,et al. An interaction map of circulating metabolites, immune gene networks, and their genetic regulation , 2016, Genome Biology.
[185] Fidele Tugizimana,et al. A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps , 2016, Metabolites.
[186] Raluca Eftimie,et al. Mathematical Models for Immunology: Current State of the Art and Future Research Directions , 2016, Bulletin of mathematical biology.
[187] Lin Shi,et al. Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction , 2016, Metabolomics.
[188] Stacy D. Sherrod,et al. Untargeted Metabolomics Strategies—Challenges and Emerging Directions , 2016, Journal of The American Society for Mass Spectrometry.
[189] M. Bauer,et al. Metabolite Profiles in Sepsis: Developing Prognostic Tools Based on the Type of Infection* , 2016, Critical care medicine.
[190] K. Reinert,et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis , 2016, Nature Methods.
[191] J. Rathmell,et al. A guide to immunometabolism for immunologists , 2016, Nature Reviews Immunology.
[192] A. Giordano,et al. Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer , 2016, Oncotarget.
[193] Antonio Cappuccio,et al. Multiscale modelling in immunology: a review , 2016, Briefings Bioinform..
[194] Min Yang,et al. An intelligentized strategy for endogenous small molecules characterization and quality evaluation of earthworm from two geographic origins by ultra-high performance HILIC/QTOF MSE and Progenesis QI , 2016, Analytical and Bioanalytical Chemistry.
[195] Yubo Li,et al. A Systematic Strategy for Screening and Application of Specific Biomarkers in Hepatotoxicity Using Metabolomics Combined With ROC Curves and SVMs. , 2016, Toxicological sciences : an official journal of the Society of Toxicology.
[196] Sun Kim,et al. MONGKIE: an integrated tool for network analysis and visualization for multi-omics data , 2016, Biology Direct.
[197] Caroline H. Johnson,et al. Metabolomics: beyond biomarkers and towards mechanisms , 2016, Nature Reviews Molecular Cell Biology.
[198] D. Wishart. Emerging applications of metabolomics in drug discovery and precision medicine , 2016, Nature Reviews Drug Discovery.
[199] Abhishek K. Jha,et al. Environment Impacts the Metabolic Dependencies of Ras-Driven Non-Small Cell Lung Cancer. , 2016, Cell metabolism.
[200] Peter D. Karp,et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases , 2015, Nucleic Acids Res..
[201] Dipak Barua,et al. BioNetGen 2.2: advances in rule-based modeling , 2015, Bioinform..
[202] Julie G. Burel,et al. Systems Approaches towards Molecular Profiling of Human Immunity. , 2016, Trends in immunology.
[203] David K. Finlay,et al. Immunometabolism: Cellular Metabolism Turns Immune Regulator* , 2015, The Journal of Biological Chemistry.
[204] Clary B. Clish,et al. Metabolomics: an emerging but powerful tool for precision medicine , 2015, Cold Spring Harbor molecular case studies.
[205] Tim Beißbarth,et al. pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge , 2015, Bioinform..
[206] Kelly V. Ruggles,et al. Profiling the Essential Nature of Lipid Metabolism in Asexual Blood and Gametocyte Stages of Plasmodium falciparum. , 2015, Cell host & microbe.
[207] A. Vicino,et al. A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response , 2015, PloS one.
[208] U. Sauer,et al. Biological insights through nontargeted metabolomics. , 2015, Current opinion in biotechnology.
[209] Adam D. Kennedy,et al. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism , 2015, Journal of Inherited Metabolic Disease.
[210] Daniel Jacob,et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics , 2014, Bioinform..
[211] Matthias Scholz,et al. MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments , 2014, Front. Bioeng. Biotechnol..
[212] C. Barbas,et al. Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues. , 2014, Bioanalysis.
[213] Maria De Iorio,et al. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN , 2014, Nature Protocols.
[214] V. Davé,et al. Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level , 2014, Cellular Physiology and Biochemistry.
[215] Charmion Cruickshank-Quinn,et al. MSPrep - Summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data , 2014, Bioinform..
[216] Mark M. Davis,et al. Systems analysis of sex differences reveals an immunosuppressive role for testosterone in the response to influenza vaccination , 2013, Proceedings of the National Academy of Sciences.
[217] E. Schleicher,et al. Simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether from a single small tissue sample for ultra-high performance liquid chromatography/mass spectrometry. , 2013, Journal of chromatography. A.
[218] T. Helikar,et al. A Cell Simulator Platform: The Cell Collective , 2013, Clinical pharmacology and therapeutics.
[219] Liang Zheng,et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α , 2013, Nature.
[220] Fabian J Theis,et al. Statistical methods for the analysis of high-throughput metabolomics data , 2013, Computational and structural biotechnology journal.
[221] M. Urpi-Sardà,et al. Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: when less is more. , 2013, Analytical chemistry.
[222] Aihua Zhang,et al. Cell metabolomics. , 2013, Omics : a journal of integrative biology.
[223] Ralf Tautenhahn,et al. An accelerated workflow for untargeted metabolomics using the METLIN database , 2012, Nature Biotechnology.
[224] M. Tomita,et al. Metabolomic study of Chilean biomining bacteria Acidithiobacillus ferrooxidans strain Wenelen and Acidithiobacillus thiooxidans strain Licanantay , 2012, Metabolomics.
[225] Natalie I. Tasman,et al. A Cross-platform Toolkit for Mass Spectrometry and Proteomics , 2012, Nature Biotechnology.
[226] Hui Sun,et al. Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease* , 2012, Molecular & Cellular Proteomics.
[227] Michelle F Clasquin,et al. LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. , 2012, Current protocols in bioinformatics.
[228] Aurélien Naldi,et al. Logical modelling of gene regulatory networks with GINsim. , 2012, Methods in molecular biology.
[229] B. Buszewski,et al. Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique , 2011, Analytical and Bioanalytical Chemistry.
[230] Joshua D. Knowles,et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry , 2011, Nature Protocols.
[231] Jianguo Xia,et al. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst , 2011, Nature Protocols.
[232] D. Mathis,et al. Immunometabolism: an emerging frontier , 2011, Nature Reviews Immunology.
[233] Yuqin Wang,et al. Targeted metabolomics for biomarker discovery. , 2010, Angewandte Chemie.
[234] Matej Oresic,et al. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data , 2010, BMC Bioinformatics.
[235] E. Want,et al. Global metabolic profiling procedures for urine using UPLC–MS , 2010, Nature Protocols.
[236] Hans A. Kestler,et al. BoolNet - an R package for generation, reconstruction and analysis of Boolean networks , 2010, Bioinform..
[237] Jing Gao,et al. Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks , 2010, Bioinform..
[238] C. Anthony Hunt,et al. Identifying the Rules of Engagement Enabling Leukocyte Rolling, Activation, and Adhesion , 2010, PLoS Comput. Biol..
[239] X. Montalban,et al. Predicting responders to therapies for multiple sclerosis , 2009, Nature Reviews Neurology.
[240] T. Hyötyläinen. Critical evaluation of sample pretreatment techniques , 2009, Analytical and bioanalytical chemistry.
[241] Tim Claridge,et al. Software Review of MNova: NMR Data Processing, Analysis, and Prediction Software , 2009, J. Chem. Inf. Model..
[242] Kellen L. Olszewski,et al. Host-parasite interactions revealed by Plasmodium falciparum metabolomics. , 2009, Cell host & microbe.
[243] Joshua D. Knowles,et al. Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. , 2009, Analytical chemistry.
[244] Doron Levy,et al. Modeling and simulation of the immune system as a self-regulating network. , 2009, Methods in enzymology.
[245] Steve Horvath,et al. WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.
[246] Jens Stoye,et al. MeltDB: a software platform for the analysis and integration of metabolomics experiment data , 2008, Bioinform..
[247] M. Viant,et al. High-throughput tissue extraction protocol for NMR- and MS-based metabolomics. , 2008, Analytical biochemistry.
[248] W. R. Wikoff,et al. Metabolomics identifies perturbations in human disorders of propionate metabolism. , 2007, Clinical chemistry.
[249] Jayajit Das,et al. The stimulatory potency of T cell antigens is influenced by the formation of the immunological synapse. , 2007, Immunity.
[250] J. Lindon,et al. Scaling and normalization effects in NMR spectroscopic metabonomic data sets. , 2006, Analytical chemistry.
[251] R. Abagyan,et al. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.
[252] J. Duffield. The inflammatory macrophage , 2003 .
[253] S. Williams,et al. A comparison of cell and tissue extraction techniques using high‐resolution 1H‐NMR spectroscopy , 2002, NMR in biomedicine.
[254] E. Adams. Amino acid metabolism. , 1962, Annual review of biochemistry.