Demo Data from VLDB 2026 / reviewers in the wild / expert
Donghai Liang
dblp:324/8690
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-7311-2298ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
metabolomics |
0.6 | 1 | 2022 | Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data · Bioinform. 2022 |
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
pathway enrichment analysis |
0.6 | 1 | 2022 | Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data · Bioinform. 2022 |
Bioinformatics and computational biology › metabolomics
untargeted metabolomics |
0.2 | 1 | 2022 | Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
permutation test · 0.6hypergeometric test · 0.6gene set enrichment analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics dataabstractMOTIVATION: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography-mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages. RESULTS: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration-(i) a weighted gene set enrichment analysis-type test and (ii) a permutation-based weighted hypergeometric test. The package is capable of combining positive- and negative-ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: Kyoto Encyclopedia of Genes and Genomes, Mummichog and The Small Molecule Pathway Database. AVAILABILITY AND IMPLEMENTATION: The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Leqi Tian, Guoxuan Ma, Ziyin Tang, Siheng Wang, Jian Kang 0003, Donghai Liang, Tianwei Yu |
Bioinform. | 8 |