{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:08:09Z","timestamp":1777428489942,"version":"3.51.4"},"reference-count":142,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"BioSys PhD programme","doi-asserted-by":"publisher","award":["SFRH\/BD\/142899\/2018"],"award-info":[{"award-number":["SFRH\/BD\/142899\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"BioSys PhD programme","doi-asserted-by":"publisher","award":["UIDB\/04046\/2020"],"award-info":[{"award-number":["UIDB\/04046\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"BioSys PhD programme","doi-asserted-by":"publisher","award":["UIDP\/04046\/2020"],"award-info":[{"award-number":["UIDP\/04046\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["SFRH\/BD\/142899\/2018"],"award-info":[{"award-number":["SFRH\/BD\/142899\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["UIDB\/04046\/2020"],"award-info":[{"award-number":["UIDB\/04046\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["UIDP\/04046\/2020"],"award-info":[{"award-number":["UIDP\/04046\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BioChem"],"abstract":"<jats:p>Metabolites are at the end of the gene\u2013transcript\u2013protein\u2013metabolism cascade. As such, metabolomics is the omics approach that offers the most direct correlation with phenotype. This allows, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics to possibly provide an answer. Complex phenotypes, which are determined by the influence of multiple small-effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased exponentially in recent years. As a newer discipline, metabolomic bioinformatic analysis pipelines are not as standardized as in the other omics approaches. In this review, we synthesized the different steps that need to be carried out to obtain biological insight from annotated metabolite abundance raw data. These steps were grouped into three different modules: preprocessing, statistical analysis, and metabolic pathway enrichment. We included within each one of them the different state-of-the-art procedures and tools that can be used depending on the characteristics of the study, providing details about each method\u2019s characteristics and the issues the reader might encounter. Finally, we introduce genome-scale metabolic modeling as a tool for obtaining pseudo-metabolomic data in situations where their acquisition is difficult, enabling the analysis of the resulting data with the modules of the described workflow.<\/jats:p>","DOI":"10.3390\/biochem4020005","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T09:16:33Z","timestamp":1713258993000},"page":"90-114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Bioinformatic Analysis of Metabolomic Data: From Raw Spectra to Biological Insight"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9012-0764","authenticated-orcid":false,"given":"Guillem","family":"Santamaria","sequence":"first","affiliation":[{"name":"BioISI\u2014Biosciences & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016 Lisboa, Portugal"},{"name":"I<sup>2<\/sup>SysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4217-0054","authenticated-orcid":false,"given":"Francisco R.","family":"Pinto","sequence":"additional","affiliation":[{"name":"BioISI\u2014Biosciences & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0167-7799(98)01214-1","article-title":"Systematic Functional Analysis of the Yeast Genome","volume":"16","author":"Oliver","year":"1998","journal-title":"Trends Biotechnol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1013713905833","article-title":"Metabolomics\u2014The Link between Genotypes and Phenotypes","volume":"48","author":"Fiehn","year":"2002","journal-title":"Plant Mol. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.trsl.2011.08.001","article-title":"Molecular Genetic Studies of Complex Phenotypes","volume":"159","author":"Marian","year":"2012","journal-title":"Transl. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3134","DOI":"10.1128\/IAI.01772-05","article-title":"Rhamnolipids Are Virulence Factors That Promote Early Infiltration of Primary Human Airway Epithelia by Pseudomonas aeruginosa","volume":"74","author":"Zulianello","year":"2006","journal-title":"Infect. Immun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1128\/JB.185.3.1027-1036.2003","article-title":"Rhamnolipid Surfactant Production Affects Biofilm Architecture in Pseudomonas aeruginosa PAO1","volume":"185","author":"Davey","year":"2003","journal-title":"J. Bacteriol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7351","DOI":"10.1128\/JB.187.21.7351-7361.2005","article-title":"Rhamnolipids Modulate Swarming Motility Patterns of Pseudomonas aeruginosa","volume":"187","author":"Caiazza","year":"2005","journal-title":"J. Bacteriol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1099\/00221287-148-10-3195","article-title":"Physiological Responses of Pseudomonas aeruginosa PAO1 to Oxidative Stress in Controlled Microaerobic and Aerobic Cultures","volume":"148","author":"Sabra","year":"2002","journal-title":"Microbiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/j.1574-6976.2011.00302.x","article-title":"Pathogenesis in Tuberculosis: Transcriptomic Approaches to Unraveling Virulence Mechanisms and Finding New Drug Targets","volume":"36","author":"Mukhopadhyay","year":"2012","journal-title":"FEMS Microbiol. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/nature12337","article-title":"The Mycobacterium tuberculosis Regulatory Network and Hypoxia","volume":"499","author":"Galagan","year":"2013","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1038\/s41598-019-40051-5","article-title":"Comparative Label-Free Lipidomic Analysis of Mycobacterium tuberculosis during Dormancy and Reactivation","volume":"9","author":"Raghunandanan","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116540","DOI":"10.1016\/j.trac.2022.116540","article-title":"Microbial Metabolomics: From Novel Technologies to Diversified Applications","volume":"148","author":"Ye","year":"2022","journal-title":"TrAC-Trends Anal. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Emwas, A.H., Roy, R., McKay, R.T., Tenori, L., Saccenti, E., Nagana Gowda, G.A., Raftery, D., Alahmari, F., Jaremko, L., and Jaremko, M. (2019). Nmr Spectroscopy for Metabolomics Research. Metabolites, 9.","DOI":"10.3390\/metabo9070123"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.trac.2007.11.004","article-title":"Comparative Evaluation of Software for Deconvolution of Metabolomics Data Based on GC-TOF-MS","volume":"27","author":"Lu","year":"2008","journal-title":"TrAC-Trends Anal. Chem."},{"key":"ref_14","first-page":"30.4.1","article-title":"Metabolomics by Gas Chromatography-Mass Spectrometry: The Combination of Targeted and Untargeted Profiling","volume":"114","author":"Fiehn","year":"2017","journal-title":"Curr. Protoc. Mol. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1093\/jat\/bkv140","article-title":"Comparison of LC-MS-MS and GC-MS Analysis of Benzodiazepine Compounds Included in the Drug Demand Reduction Urinalysis Program","volume":"40","author":"Perez","year":"2016","journal-title":"J. Anal. Toxicol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/03602530701497804","article-title":"LC-MS-Based Metabolomics in Drug Metabolism","volume":"39","author":"Chen","year":"2007","journal-title":"Drug Metab. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1021\/ac5040693","article-title":"Bioinformatics: The next Frontier of Metabolomics","volume":"87","author":"Johnson","year":"2015","journal-title":"Anal. Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/978-1-4939-2377-9_13","article-title":"The Strengths and Weaknesses of NMR Spectroscopy and Mass Spectrometry with Particular Focus on Metabolomics Research","volume":"Volume 1277","author":"Bjerrum","year":"2015","journal-title":"Metabonomics: Methods and Protocols"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1021\/acs.analchem.0c04414","article-title":"NMR: Unique Strengths That Enhance Modern Metabolomics Research","volume":"93","author":"Edison","year":"2021","journal-title":"Anal. Chem."},{"key":"ref_20","unstructured":"Jaumot, J., Bedia, C., and Tauler, R. (2018). Comprehensive Analytical Chemistry, Elsevier."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Alonso, A., Marsal, S., and Juli\u00e0, A. (2015). Analytical Methods in Untargeted Metabolomics: State of the Art in 2015. Front. Bioeng. Biotechnol., 3.","DOI":"10.3389\/fbioe.2015.00023"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"W388","DOI":"10.1093\/nar\/gkab382","article-title":"MetaboAnalyst 5.0: Narrowing the Gap between Raw Spectra and Functional Insights","volume":"49","author":"Pang","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/BF00197809","article-title":"NMRPipe: A Multidimensional Spectral Processing System Based on UNIX Pipes","volume":"6","author":"Delaglio","year":"1995","journal-title":"J. Biomol. NMR"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jmr.2007.03.017","article-title":"MatNMR: A Flexible Toolbox for Processing, Analyzing and Visualizing Magnetic Resonance Data in Matlab\u00ae","volume":"187","year":"2007","journal-title":"J. Magn. Reson."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chong, J., Yamamoto, M., and Xia, J. (2019). MetaboAnalystR 2.0: From Raw Spectra to Biological Insights. Metabolites, 9.","DOI":"10.3390\/metabo9030057"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1021\/ac051437y","article-title":"XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification","volume":"78","author":"Smith","year":"2006","journal-title":"Anal. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107320","DOI":"10.1016\/j.jmr.2022.107320","article-title":"DESPERATE: A Python Library for Processing and Denoising NMR Spectra","volume":"346","author":"Altenhof","year":"2023","journal-title":"J. Magn. Reson."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1002\/mrc.5082","article-title":"Review and Prospect: NMR Spectroscopy Denoising and Reconstruction with Low-Rank Hankel Matrices and Tensors","volume":"59","author":"Qiu","year":"2020","journal-title":"Magn. Reson. Chem."},{"key":"ref_29","first-page":"341","article-title":"Evaluation of Peak-Picking Algorithms for Protein Mass Spectrometry","volume":"Volume 696","author":"Bauer","year":"2011","journal-title":"Data Mining in Proteomics: From Standards to Applications. Methods in Molecular Biology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1093\/bioinformatics\/bts078","article-title":"WaVPeak: Picking NMR Peaks through Wavelet-Based Smoothing and Volume-Based Filtering","volume":"28","author":"Liu","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"W652","DOI":"10.1093\/nar\/gkp356","article-title":"MetaboAnalyst: A Web Server for Metabolomic Data Analysis and Interpretation","volume":"37","author":"Xia","year":"2009","journal-title":"Nucleic Acids Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xi, Y., and Rocke, D.M. (2008). Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-324"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5229","DOI":"10.1038\/s41467-021-25496-5","article-title":"DEEP Picker Is a Deep Neural Network for Accurate Deconvolution of Complex Two-Dimensional NMR Spectra","volume":"12","author":"Li","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3422","DOI":"10.1093\/bioinformatics\/btac344","article-title":"PeakBot: Machine-Learning-Based Chromatographic Peak Picking","volume":"38","author":"Bueschl","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1002\/cem.859","article-title":"Correlation Optimized Warping and Dynamic Time Warping as Preprocessing Methods for Chromatographic Data","volume":"18","author":"Tomasi","year":"2004","journal-title":"J. Chemom."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0021-9673(98)00021-1","article-title":"Aligning of Single and Multiple Wavelength Chromatographic Profiles for Chemometric Data Analysis Using Correlation Optimised Warping","volume":"805","author":"Nielsen","year":"1998","journal-title":"J. Chromatogr. A"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"259","DOI":"10.3390\/metabo3020259","article-title":"Getting Your Peaks in Line: A Review of Alignment Methods for NMR Spectral Data","volume":"3","author":"Vu","year":"2013","journal-title":"Metabolites"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"D622","DOI":"10.1093\/nar\/gkab1062","article-title":"HMDB 5.0: The Human Metabolome Database for 2022","volume":"50","author":"Wishart","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1038\/s41592-020-0942-5","article-title":"METLIN MS2 Molecular Standards Database: A Broad Chemical and Biological Resource","volume":"17","author":"Xue","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mamede, L., Fall, F., Schoumacher, M., Ledoux, A., De Tullio, P., Govaerts, B., and Fr, M. (2024). Comparison of Extraction Methods in Vitro Plasmodium falciparum: A 1H NMR and LC-MS Joined Approach. Biochem. Biophys. Res. Commun., 703.","DOI":"10.1016\/j.bbrc.2024.149684"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"S161","DOI":"10.1007\/s11306-011-0366-4","article-title":"Missing Values in Mass Spectrometry Based Metabolomics: An Undervalued Step in the Data Processing Pipeline","volume":"8","author":"Hrydziuszko","year":"2012","journal-title":"Metabolomics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/096228029900800103","article-title":"Applications of Multiple Imputation in Medical Studies: From AIDS to NHANES","volume":"8","author":"Barnard","year":"1999","journal-title":"Stat. Methods Med. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1021\/ac051495j","article-title":"Large-Scale Human Metabolomics Studies: A Strategy for Data (Pre-) Processing and Validation","volume":"78","author":"Bijlsma","year":"2006","journal-title":"Anal. Chem."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kokla, M., Virtanen, J., Kolehmainen, M., Paananen, J., and Hanhineva, K. (2019). Random Forest-Based Imputation Outperforms Other Methods for Imputing LC-MS Metabolomics Data: A Comparative Study. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-3110-0"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hong, S., and Lynn, H.S. (2020). Accuracy of Random-Forest-Based Imputation of Missing Data in the Presence of Non-Normality, Non-Linearity, and Interaction. BMC Med. Res. Methodol., 20.","DOI":"10.1186\/s12874-020-01080-1"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1186\/s40064-016-2941-7","article-title":"The Distance Function Effect on K-Nearest Neighbor Classification for Medical Datasets","volume":"5","author":"Hu","year":"2016","journal-title":"Springerplus"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1093\/bioinformatics\/bth499","article-title":"Missing Value Estimation for DNA Microarray Gene Expression Data: Local Least Squares Imputation","volume":"21","author":"Kim","year":"2005","journal-title":"Bioinformatics"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1093\/bioinformatics\/17.6.520","article-title":"Missing Value Estimation Methods for DNA Microarrays","volume":"17","author":"Troyanskaya","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1093\/bioinformatics\/btg287","article-title":"A Bayesian Missing Value Estimation Method for Gene Expression Profile Data","volume":"19","author":"Oba","year":"2003","journal-title":"Bioinformatics"},{"key":"ref_50","first-page":"1957","article-title":"Practical Approaches to Principal Component Analysis in the Presence of Missing Values","volume":"11","author":"Ilin","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nrg2825","article-title":"Tackling the Widespread and Critical Impact of Batch Effects in High-Throughput Data","volume":"11","author":"Leek","year":"2010","journal-title":"Nat. Rev. Genet."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1101\/gr.079558.108","article-title":"RNA-Seq: An Assessment of Technical Reproducibility and Comparison with Gene Expression Arrays","volume":"18","author":"Marioni","year":"2008","journal-title":"Genome Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Karpievitch, Y.V., Dabney, A.R., and Smith, R.D. (2012). Normalization and Missing Value Imputation for Label-Free LC-MS Analysis. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-S16-S5"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., and Speleman, F. (2002). Accurate Normalization of Real-Time Quantitative RT-PCR Data by Geometric Averaging of Multiple Internal Control Genes. Rock. Mech. Rock. Eng., 3.","DOI":"10.1186\/gb-2002-3-7-research0034"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1021\/acs.jproteome.6b00403","article-title":"A Proteomics Approach to the Protein Normalization Problem: Selection of Unvarying Proteins for MS-Based Proteomics and Western Blotting","volume":"15","author":"Mann","year":"2016","journal-title":"J. Proteome Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.chroma.2015.12.007","article-title":"Sample Normalization Methods in Quantitative Metabolomics","volume":"1430","author":"Wu","year":"2016","journal-title":"J. Chromatogr. A"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5342","DOI":"10.1021\/acs.analchem.6b05152","article-title":"Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?","volume":"89","author":"Chen","year":"2017","journal-title":"Anal. Chem."},{"key":"ref_58","first-page":"1","article-title":"Cross-Platform Urine Metabolomics of Experimental Hyperglycemia in Type 2 Diabetes","volume":"6","author":"Temmerman","year":"2012","journal-title":"J. Diabetes Metab."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1021\/ac502439y","article-title":"Statistical Methods for Handling Unwanted Variation in Metabolomics Data","volume":"87","author":"Jacob","year":"2015","journal-title":"Anal. Chem."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"10925","DOI":"10.1021\/ac503190m","article-title":"Normalization to Specific Gravity Prior to Analysis Improves Information Recovery from High Resolution Mass Spectrometry Metabolomic Profiles of Human Urine","volume":"86","author":"Edmands","year":"2014","journal-title":"Anal. Chem."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.mimet.2011.07.001","article-title":"Optimization of a Sample Preparation Method for the Metabolomic Analysis of Clinically Relevant Bacteria","volume":"87","author":"Marcinowska","year":"2011","journal-title":"J. Microbiol. Methods"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"7659","DOI":"10.1021\/ac401400b","article-title":"Combination of Injection Volume Calibration by Creatinine and MS Signals\u2019 Normalization to Overcome Urine Variability in LC-MS-Based Metabolomics Studies","volume":"85","author":"Chen","year":"2013","journal-title":"Anal. Chem."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"10768","DOI":"10.1021\/ac302748b","article-title":"Normalizing and Integrating Metabolomics Data","volume":"84","author":"Dias","year":"2012","journal-title":"Anal. Chem."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Antonelli, J., Claggett, B.L., Henglin, M., Kim, A., Ovsak, G., Kim, N., Deng, K., Rao, K., Tyagi, O., and Watrous, J.D. (2019). Statistical Workflow for Feature Selection in Human Metabolomics Data. Metabolites, 9.","DOI":"10.3390\/metabo9070143"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"van den Berg, R.A., Hoefsloot, H.C.J., Westerhuis, J.A., Smilde, A.K., and van der Werf, M.J. (2006). Centering, Scaling, and Transformations: Improving the Biological Information Content of Metabolomics Data. BMC Genom., 7.","DOI":"10.1186\/1471-2164-7-142"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11306-018-1347-7","article-title":"NormalizeMets: Assessing, Selecting and Implementing Statistical Methods for Normalizing Metabolomics Data","volume":"14","author":"Olshansky","year":"2018","journal-title":"Metabolomics"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sysi-Aho, M., Katajamaa, M., Yetukuri, L., and Ore\u0161i\u010d, M. (2007). Normalization Method for Metabolomics Data Using Optimal Selection of Multiple Internal Standards. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-93"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Grocholska, P., and Bachor, R. (2021). Trends in the Hydrogen\u2212deuterium Exchange at the Carbon Centers. Preparation of Internal Standards for Quantitative Analysis by Lc-Ms. Molecules, 26.","DOI":"10.3390\/molecules26102989"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ab.2004.04.037","article-title":"Design of Experiments: An Efficient Strategy to Identify Factors Influencing Extraction and Derivatization of Arabidopsis thaliana Samples in Metabolomic Studies with Gas Chromatography\/Mass Spectrometry","volume":"331","author":"Gullberg","year":"2004","journal-title":"Anal. Biochem."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"618A","DOI":"10.1021\/ac022161m","article-title":"Isotopically Labeled Analogues for Drug Quantitation","volume":"74","author":"Liu","year":"2002","journal-title":"Anal. Chem."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"7974","DOI":"10.1021\/ac901143w","article-title":"Compensation for Systematic Cross-Contribution Improves Normalization of Mass Spectrometry Based Metabolomics Data","volume":"81","author":"Redestig","year":"2009","journal-title":"Anal. Chem."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1093\/biostatistics\/kxr034","article-title":"Using Control Genes to Correct for Unwanted Variation in Microarray Data","volume":"13","author":"Speed","year":"2012","journal-title":"Biostatistics"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e76119","DOI":"10.7554\/eLife.76119","article-title":"Evolution and Regulation of Microbial Secondary Metabolism","volume":"11","author":"Santamaria","year":"2022","journal-title":"eLife"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1038\/nprot.2011.335","article-title":"Procedures for Large-Scale Metabolic Profiling of Serum and Plasma Using Gas Chromatography and Liquid Chromatography Coupled to Mass Spectrometry","volume":"6","author":"Dunn","year":"2011","journal-title":"Nat. Protoc."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1039\/b604498k","article-title":"A Pragmatic and Readily Implemented Quality Control Strategy for HPLC-MS and GC-MS-Based Metabonomic Analysis","volume":"131","author":"Sangster","year":"2006","journal-title":"Analyst"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1021\/pr070183p","article-title":"Within-Day Reproducibility of an HPLC-MS-Based Method for Metabonomic Analysis: Application to Human Urine","volume":"6","author":"Gika","year":"2007","journal-title":"J. Proteome Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s11306-018-1367-3","article-title":"Guidelines and Considerations for the Use of System Suitability and Quality Control Samples in Mass Spectrometry Assays Applied in Untargeted Clinical Metabolomic Studies","volume":"14","author":"Broadhurst","year":"2018","journal-title":"Metabolomics"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Schiffman, C., Petrick, L., Perttula, K., Yano, Y., Carlsson, H., Whitehead, T., Metayer, C., Hayes, J., Rappaport, S., and Dudoit, S. (2019). Filtering Procedures for Untargeted Lc-Ms Metabolomics Data. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2871-9"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7038","DOI":"10.1021\/ac9011599","article-title":"Development and Performance of a Gas Chromatography-Time-of-Flight Mass Spectrometry Analysis for Large-Scale Nontargeted Metabolomic Studies of Human Serum","volume":"81","author":"Begley","year":"2009","journal-title":"Anal. Chem."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1021\/ac8019366","article-title":"Development of a Robust and Repeatable UPLC-MS Method for the Long-Term Metabolomic Study of Human Serum","volume":"81","author":"Zelena","year":"2009","journal-title":"Anal. Chem."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_82","unstructured":"De Livera, A.M., Olshansky, M., and Speed, T.P. (2013). Metabolomics Tools for Natural Product Discovery, Humana Press."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1038\/s41592-020-0772-5","article-title":"Author Correction: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_85","unstructured":"Sokal, R.R., and Rohlf, F.J. (1995). Biometry. The Principles and Practice of Statistics in Biological Research, W. H. Freeman and Company. [3rd ed.]."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/cc3045","article-title":"Statistics Review 14: Logistic Regression","volume":"9","author":"Bewick","year":"2005","journal-title":"Crit. Care"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11306-006-0037-z","article-title":"Statistical Strategies for Avoiding False Discoveries in Metabolomics and Related Experiments","volume":"2","author":"Broadhurst","year":"2006","journal-title":"Metabolomics"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11306-013-0598-6","article-title":"Reflections on Univariate and Multivariate Analysis of Metabolomics Data","volume":"10","author":"Saccenti","year":"2014","journal-title":"Metabolomics"},{"key":"ref_89","first-page":"92","article-title":"Multivariate Analysis in Metabolomics","volume":"1","author":"Worley","year":"2013","journal-title":"Curr. Metabolomics"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1038\/srep06207","article-title":"Critical Limitations of Consensus Clustering in Class Discovery","volume":"4","author":"Michailidis","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1038\/s41598-020-58766-1","article-title":"M3C: Monte Carlo Reference-Based Consensus Clustering","volume":"10","author":"John","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1093\/molbev\/msx174","article-title":"Metabolism and the Evolution of Social Behavior","volume":"34","author":"Boyle","year":"2017","journal-title":"Mol. Biol. Evol."},{"key":"ref_93","first-page":"2579","article-title":"Visualizing Data Using T-SNE","volume":"219","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_94","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1093\/bioinformatics\/btq170","article-title":"ConsensusClusterPlus: A Class Discovery Tool with Confidence Assessments and Item Tracking","volume":"26","author":"Wilkerson","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-Regression: A Basic Tool of Chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1002\/cem.695","article-title":"Orthogonal Projections to Latent Structures (O-PLS)","volume":"16","author":"Trygg","year":"2002","journal-title":"J. Chemom."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.chemolab.2012.07.010","article-title":"A Review of Variable Selection Methods in Partial Least Squares Regression","volume":"118","author":"Mehmood","year":"2012","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Rizvi, A., Shankar, A., Chatterjee, A., More, T.H., Bose, T., Dutta, A., Balakrishnan, K., Madugulla, L., Rapole, S., and Mande, S.S. (2019). Rewiring of Metabolic Network in Mycobacterium tuberculosis during Adaptation to Different Stresses. Front. Microbiol., 10.","DOI":"10.3389\/fmicb.2019.02417"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"22525","DOI":"10.1038\/srep22525","article-title":"Integrated Metabolomics and Metagenomics Analysis of Plasma and Urine Identified Microbial Metabolites Associated with Coronary Heart Disease","volume":"6","author":"Feng","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fphar.2016.00014","article-title":"Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Cholestasis and Intervention Effect of Paeonia lactiflora Pall","volume":"7","author":"Ma","year":"2016","journal-title":"Front. Pharmacol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1752-153X-2-21","article-title":"Simultaneous Feature Selection and Parameter Optimisation Using an Artificial Ant Colony: Case Study of Melting Point Prediction","volume":"2","author":"Palmer","year":"2008","journal-title":"Chem. Cent. J."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11306-011-0330-3","article-title":"Double-Check: Validation of Diagnostic Statistics for PLS-DA Models in Metabolomics Studies","volume":"8","author":"Saccenti","year":"2012","journal-title":"Metabolomics"},{"key":"ref_104","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, CRC."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1162\/neco.1997.9.7.1545","article-title":"Shape Quantization and Recognition with Randomized Trees","volume":"9","author":"Amit","year":"1997","journal-title":"Neural Comput."},{"key":"ref_108","first-page":"2015","article-title":"Consistency of Random Forests and Other Averaging Classifiers","volume":"9","author":"Devroye","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1021\/acs.jproteome.5b00354","article-title":"Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses","volume":"14","author":"Roux","year":"2015","journal-title":"J. Proteome Res."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Mevik, B.-H., and Wehrens, R. (2007). The Pls Package: Principal Component and Partial Least Squares Regression in R. J. Stat. Softw., 18.","DOI":"10.18637\/jss.v018.i02"},{"key":"ref_112","unstructured":"(2024, April 15). BiRG\u2014Wright State University Pyopls. Available online: https:\/\/pypi.org\/project\/pyopls\/."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the Caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_114","first-page":"18","article-title":"Classification and Regression by RandomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Khatri, P., Sirota, M., and Butte, A.J. (2012). Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1002375"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1093\/bioinformatics\/btm051","article-title":"Analyzing Gene Expression Data in Terms of Gene Sets: Methodological Issues","volume":"23","author":"Goeman","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"ClusterProfiler: An R Package for Comparing Biological Themes among Gene Clusters","volume":"16","author":"Yu","year":"2012","journal-title":"Omics J. Integr. Biol."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"654","DOI":"10.3389\/fgene.2020.00654","article-title":"Gene Set Analysis: Challenges, Opportunities, and Future Research","volume":"11","author":"Maleki","year":"2020","journal-title":"Front. Genet."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/ng1180","article-title":"PGC-1\u03b1-Responsive Genes Involved in Oxidative Phosphorylation Are Coordinately Downregulated in Human Diabetes","volume":"34","author":"Mootha","year":"2003","journal-title":"Nat. Genet."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Pang, Z., Chong, J., Li, S., and Xia, J. (2020). MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics. Metabolites, 10.","DOI":"10.3390\/metabo10050186"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Tomfohr, J., Lu, J., and Kepler, T.B. (2005). Pathway Level Analysis of Gene Expression Using Singular Value Decomposition. BMC Bioinform., 6.","DOI":"10.1186\/1471-2105-6-225"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"McLuskey, K., Wandy, J., Vincent, I., van der Hooft, J.J.J., Rogers, S., Burgess, K., and Daly, R. (2021). Ranking Metabolite Sets by Their Activity Levels. Metabolites, 11.","DOI":"10.3390\/metabo11020103"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1089\/cmb.2008.0081","article-title":"Analysis of Gene Sets Based on the Underlying Regulatory Network","volume":"16","author":"Shojaie","year":"2009","journal-title":"J. Comput. Biol."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Hellstern, M., Ma, J., Yue, K., and Shojaie, A. (2021). Netgsa: Fast Computation and Interactive Visualization for Topology-Based Pathway Enrichment Analysis. PLoS Comput. Biol., 17.","DOI":"10.1371\/journal.pcbi.1008979"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1186\/s12859-018-2487-5","article-title":"FELLA: An R Package to Enrich Metabolomics Data","volume":"19","author":"Vinaixa","year":"2018","journal-title":"BMC Bioinform."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1214\/11-AOAS528","article-title":"More Power via Graph-Structured Tests for Differential Expression of Gene Networks","volume":"6","author":"Jacob","year":"2012","journal-title":"Ann. Appl. Stat."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Santamaria, G., Ruiz-Rodr\u00edguez, P., Renau-M\u00ednguez, C., Pinto, F.R., and Coscoll\u00e1, M. (2022). In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes. Biomolecules, 12.","DOI":"10.3390\/biom12030376"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Christodoulides, M. (2011). Neisseria meningiditis: Advanced Methods and Protocols, Humana Press.","DOI":"10.1007\/978-1-61779-346-2"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"14631","DOI":"10.1038\/ncomms14631","article-title":"Reconstruction of the Metabolic Network of Pseudomonas aeruginosa to Interrogate Virulence Factor Synthesis","volume":"8","author":"Bartell","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_130","first-page":"17410","article-title":"Systems Properties of the Haemophilus Influenzae Rd Metabolic Genbotype","volume":"274","author":"Edwards","year":"1999","journal-title":"Mol. Biol."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Karp, P.D., Weaver, D., and Latendresse, M. (2018). How Accurate Is Automated Gap Filling of Metabolic Models?. BMC Syst. Biol., 12.","DOI":"10.1186\/s12918-018-0593-7"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Palsson, B.\u00d8. (2006). Systems Biology: Properties of Reconstructed Networks, Cambridge University Press.","DOI":"10.1017\/CBO9780511790515"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1038\/nbt1094-994","article-title":"Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use","volume":"12","author":"Varma","year":"1994","journal-title":"Nat. Biotechnol."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.mib.2010.03.003","article-title":"The Biomass Objective Function","volume":"13","author":"Feist","year":"2010","journal-title":"Curr. Opin. Microbiol."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Schuetz, R., Kuepfer, L., and Sauer, U. (2007). Systematic Evaluation of Objective Functions for Predicting Intracellular Fluxes in Escherichia Coli. Mol. Syst. Biol., 3.","DOI":"10.1038\/msb4100162"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"4518","DOI":"10.1128\/IAI.68.8.4518-4522.2000","article-title":"Growth of Mycobacterium tuberculosis in a Defined Medium Is Very Restricted by Acid pH and Mg2+ Levels Mycobacterium tuberculosis Grows within the Phagocytic Vacuoles of Macrophages, Where It Encounters a Moderately Acidic and Possibly Nutrient-Restricted","volume":"68","author":"Piddington","year":"2000","journal-title":"Infect. Immun."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Boyle, K.E., Monaco, H., van Ditmarsch, D., Deforet, M., and Xavier, J.B. (2015). Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait. PLoS Comput. Biol., 11.","DOI":"10.1371\/journal.pcbi.1004279"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Herrmann, H.A., Dyson, B.C., Vass, L., Johnson, G.N., and Schwartz, J.M. (2019). Flux Sampling Is a Powerful Tool to Study Metabolism under Changing Environmental Conditions. npj Syst. Biol. Appl., 5.","DOI":"10.1038\/s41540-019-0109-0"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.jtbi.2004.02.006","article-title":"Monte Carlo Sampling Can Be Used to Determine the Size and Shape of the Steady-State Flux Space","volume":"228","author":"Wiback","year":"2004","journal-title":"J. Theor. Biol."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1038\/nrd.2016.32","article-title":"Emerging Applications of Metabolomics in Drug Discovery and Precision Medicine","volume":"15","author":"Wishart","year":"2016","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"8494","DOI":"10.1073\/pnas.1915551117","article-title":"Model-Based Integration of Genomics and Metabolomics Reveals SNP Functionality in Mycobacterium tuberculosis","volume":"117","author":"Borrell","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"e2217868120","DOI":"10.1073\/pnas.2217868120","article-title":"Generation and Analysis of Context-Specific Genome-Scale Metabolic Models Derived from Single-Cell RNA-Seq Data","volume":"120","author":"Gustafsson","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["BioChem"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-6411\/4\/2\/5\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:28:42Z","timestamp":1760106522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-6411\/4\/2\/5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":142,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["biochem4020005"],"URL":"https:\/\/doi.org\/10.3390\/biochem4020005","relation":{},"ISSN":["2673-6411"],"issn-type":[{"value":"2673-6411","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,16]]}}}