ArticleCurrent Metabolomics2013
Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks.
Article in Current Metabolomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review.Pharmaceuticals (Basel, Switzerland) · 2021Review
- OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security.Plants (Basel, Switzerland) · 2021Review
- Metabolomic Approaches Reveal the Role of CAR in Energy Metabolism.Journal of proteome research · 2019Article
- Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology.International journal of molecular sciences · 2016Review
- Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach.Bioinformatics (Oxford, England) · 2014Article
- Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data.Journal of proteome research · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.