VLDB 2026 Research / reviewers in the wild / expert
Patricia Severino
dblp:49/8393
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
0000-0002-6682-9343ORCID · reported
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › biological network › network biology › network inference
gene regulatory network inference |
0.1 | 1 | 2010 | A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets · Bioinform. 2010 |
Bioinformatics and computational biology › biological network › network biology › network inference › gene regulatory network inference
granger causality |
0.1 | 1 | 2010 | A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets · Bioinform. 2010 |
Bioinformatics and computational biology › statistical genetics
likelihood ratio test |
0.1 | 1 | 2010 | A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets · Bioinform. 2010 |
Methods — techniques the papers use, named apart from their topics
likelihood ratio test · 0.1bootstrap · 0.1bartlett correction · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene setsabstractAbstract Summary: We propose a likelihood ratio test (LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrap-based approach. LRT is shown to be significantly faster and statistically powerful even within non-Normal distributions. An R package named gGranger containing an implementation for both Granger causality identification tests is also provided. Availability: http://dnagarden.ims.u-tokyo.ac.jp/afujita/en/doku.php?id=ggranger. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. André Fujita, Kaname Kojima, Alexandre Galvão Patriota, João R. Sato, Patricia Severino, Satoru Miyano |
Bioinform. | 5 |