Demo Data from EDBT 2026 / reviewers in the wild / expert
Jan Urban
dblp:95/7431
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2009
0000-0002-5804-1679ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › metabolomics
LC-MS data analysis |
0.1 | 1 | 2009 | Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approach · Bioinform. 2009 |
Bioinformatics and computational biology
metabolomics |
0.1 | 1 | 2009 | Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approach · Bioinform. 2009 |
Methods — techniques the papers use, named apart from their topics
stochastic systems approach · 0.1noise modeling · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approachabstractUNLABELLED: Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output into three components: (i) signature of the analyte, (ii) random noise and (iii) systemic noise. The systemic noise is related to the instrument and to the particular experiment; its characteristics change in time and depend on the analyzed substance. Working with these components allows us to quantify the probability of peak errors and, at the same time, to retrieve some peaks which get lost in the noise when using the existing methods. Our software tool, Expertomica metabolite profiling, automatically evaluates the given instrument, detects compounds and calculates the probability of individual peaks. It does not need any artificial user-defined parameters or thresholds. AVAILABILITY: MATLAB scripts with a simple graphical user interface are free to download from http://sourceforge.net/projects/expertomica-eda/. The software reads data exported by most Thermo and Agilent spectrometers, and it can also read the more general JCAMP-DX ASCII format. Other formats will be supported on request, assuming that the user can provide representative data samples. Jan Urban, Jan Vanek, Jiri Soukup, Dalibor Stys |
Bioinform. | 1 |