VLDB 2026 Research / reviewers in the wild / expert
Augustin Scalbert
dblp:124/0994
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
biomarker discovery |
0.2 | 1 | 2015 | MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets · Bioinform. 2015 |
Bioinformatics and computational biology › metabolomics
LC-MS data analysis |
0.2 | 1 | 2015 | MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets · Bioinform. 2015 |
Bioinformatics and computational biology
metabolomics |
0.2 | 1 | 2015 | MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets · Bioinform. 2015 |
Bioinformatics and computational biology › metabolomics
metabolite identification |
0.1 | 1 | 2015 | MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets · Bioinform. 2015 |
Methods — techniques the papers use, named apart from their topics
signal smoothing · 0.2normalization · 0.2hierarchical clustering · 0.2PCA · 0.2MS/MS spectral matching · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasetsabstractUNLABELLED: MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). AVAILABILITY AND IMPLEMENTATION: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. William M. B. Edmands, Dinesh Kumar Barupal, Augustin Scalbert |
Bioinform. | 3 |