Special Issue "Data Processing in Metabolomics"
A special issue of Metabolites (ISSN 2218-1989).
Deadline for manuscript submissions: 31 July 2013
Dr. Jan Baumbach
Computational Systems Biology, Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
Interests: metabolomics; biomarker discovery; disease classification; ion mobility spectrometry
All living cells, throughout all domains of life, control their metabolism in accordance to internal and external conditions. Increasing sensitivity of modern high-throughput spectrometry and chromatography technologies allows us to measure a large fraction of the metabolome of cells, tissues and organs on different scales. The research area of metabolic profiling emerged with a tremendous impact on biomedical research. The hope is to unravel molecular decision processes underlying growth, survival, reproduction and differentiation of cells, tissues, organs and microbial colonies under varying conditions. On a larger scale, we enter the fields of personalized medicine, biomarker discovery & validation as well as therapy optimization. However, the cheaper and faster our technology is, the more data is generated that needs to be analyzed efficiently regarding real-world questions, such as diagnostics, prognosis and in silico modeling of the cell behavior. This special issue will focus on detailing the emerging problems and novel approaches in metabolomics data analysis.
We will consider research papers with special focus on computational methods for analyzing data from typical metabolomics technologies, such as de-noising, smoothing, peak detection, metabolic database design & data integration, disease classification, biomarker identification & validation, disease-sub-typing, data reduction, and metabolic profile alignment. Manuscripts about closely related topics, metabolic network simulation, for instance, are also welcome.
Dr. Jan Baumbach
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed Open Access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
- metabolic profiles
- metabolic patterns
- biomarker detection
- biomarker validation
- personalized medicine
- therapy optimization
- metabolic modeling
- metabolic network simulation
- data integration
- integrated databases
Article: Validated and Predictive Processing of Gas Chromatography-Mass Spectrometry Based Metabolomics Data for Large Scale Screening Studies, Diagnostics and Metabolite Pattern Verification
Metabolites 2012, 2(4), 796-817; doi:10.3390/metabo2040796
Received: 11 September 2012; in revised form: 15 October 2012 / Accepted: 16 October 2012 / Published: 31 October 2012| Download PDF Full-text (622 KB) | Download XML Full-text |
Metabolites 2013, 3(1), 155-167; doi:10.3390/metabo3010155
Received: 6 December 2012; in revised form: 5 February 2013 / Accepted: 5 March 2013 / Published: 11 March 2013| Download PDF Full-text (556 KB)
Article: Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches
Metabolites 2013, 3(2), 277-293; doi:10.3390/metabo3020277
Received: 25 January 2013; in revised form: 15 March 2013 / Accepted: 9 April 2013 / Published: 16 April 2013| Download PDF Full-text (424 KB)
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Type of Paper: Article
Title: Predictive Loadings Comparison: Evaluation and Visualization of Jackknife Confidence Estimation in NMR Metabolomics
Authors: Gavin E Duggan 1, Jane Shearer 2, Raylene Reimer 2, Yarrow Mcconnell 1 and Aalim M. Weljie 3
Affiliations: 1 Department of Biological Sciences, University of Calgary, Alberta, Canada
2 Faculty of Kinesiology, University of Calgary, Alberta, Canada
3 Department of Pharmacology, University of Pennsylvania, USA
Abstract: Jackknifing is a widely implemented variable selection (VS) technique. It has been suggested as a way to clarify metabolomics results, but validating its effectiveness in multivariate results is not intuitive. To investigate the relationship between statistical significance (of either profiled metabolites or spectral bins) and conserved response across replicates, we developed an alternative application of orthogonalized PLS loadings which results in a Predictive Loadings Comparison (PLC) plot. In each of three real-world datasets, OPLS loadings for two identical replicates were juxtaposed using linear regression. Replicate inconsistency was estimated using the error in the linear regression and compared to the statistical significance predicted by a t-tests of the jackknifed standard error. The resulting PLC plots show that simple jackknife standard errors do not translate easily into meaningful confidence estimates for metabolites or bins, and more sophisticated variable selection techniques should be evaluated.
Last update: 17 December 2012