Demo Data from VLDB 2026 / reviewers in the wild / expert
Bongsong Kim
dblp:244/9069
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
genome-wide association study |
0.4 | 1 | 2019 | GWASpro: a high-performance genome-wide association analysis server · Bioinform. 2019 |
Bioinformatics and computational biology
genomics |
0.4 | 1 | 2019 | GWASpro: a high-performance genome-wide association analysis server · Bioinform. 2019 |
Bioinformatics and computational biology › statistical genetics
linear mixed model |
0.4 | 1 | 2019 | GWASpro: a high-performance genome-wide association analysis server · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
linear mixed model · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | GWASpro: a high-performance genome-wide association analysis serverabstractSUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Bongsong Kim, Xinbin Dai, Zhaohong Zhuang, Darlene L. Sanchez, Thomas Lübberstedt, Yun Kang, Michael K. Udvardi, William D. Beavis, Shizhong Xu, Patrick Xuechun Zhao |
Bioinform. | 1 |