Enhancing metabolomics research through data mining
Journal of Proteomics, ISSN: 1874-3919, Vol: 127, Issue: Pt B, Page: 275-288
2015
- 94Citations
- 155Captures
- 1Mentions
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.
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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
- Citations94
- Citation Indexes94
- 94
- CrossRef87
- Captures155
- Readers155
- 155
- Mentions1
- References1
- Wikipedia1
Article Description
Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1874391915000445; http://dx.doi.org/10.1016/j.jprot.2015.01.019; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84944155380&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/25668325; https://linkinghub.elsevier.com/retrieve/pii/S1874391915000445; https://dx.doi.org/10.1016/j.jprot.2015.01.019
Elsevier BV
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