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Metabolites
Special Issues
Selected Papers from the 4th International Electronic Conference on Metabolomics
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Selected Papers from the 4th International Electronic Conference on Metabolomics

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1262

Special Issue Editors

Prof. Dr. Chi Chen

E-Mail Website
Guest Editor
Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, USA
Interests: metabolomics; nutrient metabolism; nutritional biochemistry; xenobiotic metabolism; microbial metabolism; animal nutrition
Special Issues, Collections and Topics in MDPI journals
Dr. Reza M. Salek

E-Mail Website
Guest Editor
School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0SP, UK
Interests: bioinformatics; computational biology; systems biology; metabolomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolites held the 4th International Electronic Conference of Metabolomics (https://sciforum.net/event/IECM2025) online from 13 to 15 Oct 2025. The conference will feature six dynamic sessions exploring diverse aspects of metabolomics:

  • Clinical Metabolomics: Unlocking Insights for Endocrinological Disorders;
  • Pioneering Technological Frontiers in Metabolomics;
  • Breaking Barriers with Advanced Data Analysis in Metabolomics;
  • Precision Nutrition Meets Metabolomics: Transforming Human and Animal Health;
  • Metabolomics in Drug Discovery: Shaping the Future of Pharmacology;
  • Microbial Metabolites: Novel Pathways to Health and Therapeutics.

Part of the Special Issue will include selected invited contributions from the 4th International Electronic Conference on Metabolomics (IECM2025). We also encourage other contributions from the broader metabolomics community.

Prof. Dr. Chi Chen
Dr. Reza M. Salek
Guest Editors

Manuscript Submission Information

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. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind 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 monthly 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 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clinical metabolomics
  • metabolomic technology
  • data analysis
  • nutrition
  • drug discovery
  • microbial metabolites

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Published Papers (1 paper)

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Research

25 pages, 4726 KB  
Article
Information-Content-Informed Kendall-Tau Correlation Methodology: Interpreting Missing Values in Metabolomics as Potentially Useful Information
by Robert M. Flight, Praneeth S. Bhatt and Hunter N. B. Moseley
Metabolites 2026, 16(4), 245; https://doi.org/10.3390/metabo16040245 - 4 Apr 2026
Viewed by 424
Abstract
Background: Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will [...] Read more.
Background: Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will be impacted based on the perspective that the missing data represents no useful information. However, missing values occur in real datasets for a variety of reasons. In metabolomics datasets a major reason for missing values is that a specific measurable phenomenon falls below the detection limits of the analytical instrumentation (left-censored values). These missing data are not missing at random, but represent potentially useful information by virtue of their “missingness” at one end of the data distribution. Methods: To include this information due to left-censored missingness, we propose the information-content-informed Kendall-tau (ICI-Kt) methodology. We develop a statistical test and then show that most missing values in metabolomics datasets are the result of left-censorship. Next, we show how left-censored missing values can be included within the definition of the Kendall-tau correlation coefficient, and how that inclusion leads to an interpretation of information being added to the correlation. We also implement calculations for additional measures of theoretical maxima and pairwise completeness that add further layers of information interpretation in the methodology. Results: Using both simulated and over 700 experimental data sets from the Metabolomics Workbench, we demonstrate that the ICI-Kt methodology allows for the inclusion of left-censored missing data values as interpretable information, enabling both improved determination of outlier samples and improved feature–feature network construction. Conclusions: We provide explicitly parallel implementations in both R and Python that allow fast calculations of all the variables used when applying the ICI-Kt methodology on large numbers of samples. The ICI-Kt methods are available as an R package and Python module on GitHub. Full article
(This article belongs to the Special Issue Selected Papers from the 4th International Electronic Conference on Metabolomics)
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