VLDB 2026 Research / reviewers in the wild / expert
Sasha Sa
dblp:424/3940
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › proteomics
quantitative proteomics |
0.5 | 1 | 2021 | obaDIA: one-step biological analysis pipeline for data-independent acquisition and other quantitative proteomics data · Bioinform. 2021 |
Bioinformatics and computational biology › gene expression analysis
differential expression analysis |
0.1 | 1 | 2021 | obaDIA: one-step biological analysis pipeline for data-independent acquisition and other quantitative proteomics data · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
functional annotation · 0.5enrichment analysis · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | obaDIA: one-step biological analysis pipeline for data-independent acquisition and other quantitative proteomics dataabstractMOTIVATION: Data mining and data quality evaluation are indispensable constituents of quantitative proteomics, but few integrated tools available. RESULTS: We introduced obaDIA, a one-step pipeline to generate visualizable and comprehensive results for quantitative proteomics data. obaDIA supports fragment-level, peptide-level and protein-level abundance matrices from DIA technique, as well as protein-level abundance matrices from other quantitative proteomic techniques. The result contains abundance matrix statistics, differential expression analysis, protein functional annotation and enrichment analysis. Additionally, enrichment strategies which use total proteins or expressed proteins as background are optional, and HTML based interactive visualization for differentially expressed proteins in the KEGG pathway is offered, which helps biological significance mining. In short, obaDIA is an automatic tool for bioinformatics analysis for quantitative proteomics. AVAILABILITY AND IMPLEMENTATION: obaDIA is freely available from https://github.com/yjthu/obaDIA.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Hongning Zhai, Sasha Sa |
Bioinform. | 4 |