Metabolomics as a tool to understand pathophysiological processes
Methods in Molecular Biology, ISSN: 1064-3745, Vol: 1730, Page: 3-28
2018
- 42Citations
- 41Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations42
- Citation Indexes42
- 42
- CrossRef3
- Captures41
- Readers41
- 41
Book Chapter Description
Multiple diseases have a strong metabolic component, and metabolomics as a powerful phenotyping technology, in combination with orthogonal biological and clinical approaches, will undoubtedly play a determinant role in accelerating the understanding of mechanisms that underlie these complex diseases determined by a set of genetic, lifestyle, and environmental exposure factors. Here, we provide several examples of valuable findings from metabolomics-led studies in diabetes and obesity metabolism, neurodegenerative disorders, and cancer metabolism and offer a longer term vision toward personalized approach to medicine, from population-based studies to pharmacometabolomics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85041109336&origin=inward; http://dx.doi.org/10.1007/978-1-4939-7592-1_1; http://www.ncbi.nlm.nih.gov/pubmed/29363062; http://link.springer.com/10.1007/978-1-4939-7592-1_1; https://dx.doi.org/10.1007/978-1-4939-7592-1_1; https://link.springer.com/protocol/10.1007/978-1-4939-7592-1_1
Springer Nature America, Inc
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