The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Frontiers of high-throughput metabolomics
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Cites
An atlas of genetic influences on human blood metabolites
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin, S.-Y., Fauman, E. B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., et al. (2014) · Nature Genetics · doi:10.1038/ng.2982 · Cited by 1,515
Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation
Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A. L., Kafri, R., Kirschner, M. W., Clish, C. B., & Mootha, V. K. (2012) · Science · doi:10.1126/science.1218595 · Cited by 1,403
Personal omics profiling reveals dynamic molecular and medical phenotypes
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., et al. (2012) · Cell · doi:10.1016/j.cell.2012.02.009 · Cited by 1,291
Methods of integrating data to uncover genotype-phenotype interactions
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832
Kettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A. J., Lyytikäinen, L.-P., Pirinen, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11122 · Cited by 738
Functional genomic hypothesis generation and experimentation by a robot scientist
King, R. D., Whelan, K. E., Jones, F. M., Reiser, P. G. K., Bryant, C. H., Muggleton, S. H., Kell, D. B., & Oliver, S. G. (2004) · Nature · doi:10.1038/nature02236 · Cited by 662
The genetics of plant metabolism
Keurentjes, J. J. B., Fu, J., de Vos, C. H. R., Lommen, A., Hall, R. D., Bino, R. J., van der Plas, L. H. W., Jansen, R. C., Vreugdenhil, D., & Koornneef, M. (2006) · Nature Genetics · doi:10.1038/ng1815 · Cited by 499
Dynamic profiling of the protein life cycle in response to pathogens
Jovanovic, M., Rooney, M. S., Mertins, P., Przybylski, D., Chevrier, N., Satija, R., Rodriguez, E. H., Fields, A. P., Schwartz, S., Raychowdhury, R., et al. (2015) · Science · doi:10.1126/science.1259038 · Cited by 460
Metabolite concentrations, fluxes and free energies imply efficient enzyme usage
Park, J. O., Rubin, S. A., Xu, Y.-F., Amador-Noguez, D., Fan, J., Shlomi, T., & Rabinowitz, J. D. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2077 · Cited by 453
Cited by
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
Image-based profiling for drug discovery: due for a machine-learning upgrade?
Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., & Carpenter, A. E. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-00117-w · Cited by 389
Machine learning for metabolic engineering: A review
Lawson, C. E., Martí, J. M., Radivojevic, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., et al. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2020.10.005 · Cited by 313
Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online
Forsberg, E. M., Huan, T., Rinehart, D., Benton, H. P., Warth, B., Hilmers, B., & Siuzdak, G. (2018) · Nature Protocols · doi:10.1038/nprot.2017.151 · Cited by 266
Synthetic microbial consortia for biosynthesis and biodegradation: promises and challenges
Che, S., & Men, Y. (2019) · Journal of Industrial Microbiology and Biotechnology · doi:10.1007/s10295-019-02211-4 · Cited by 177
Metadata
- Authors
- Zampieri, M., Sekar, K., Zamboni, N., & Sauer, U.
- Venue
- Current Opinion in Chemical Biology
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 36
- Pages
- 15-23
- ISSN
- 1367-5931 (ISSN-L)
- OpenAlex
- W2571021552
- Cited by
- 172
- References
- 14
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Frontiers of high-throughput metabolomics. https://app.cochat.ai/papers/10.1016/j.cbpa.2016.12.006
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- Zampieri, M., Sekar, K., Zamboni, N., & Sauer, U. (2017). Frontiers of high-throughput metabolomics. Current Opinion in Chemical Biology. https://doi.org/10.1016/j.cbpa.2016.12.006
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
