Demo Data from EDBT 2026 / reviewers in the wild / expert
Shi-Jie Chen
dblp:274/0203
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-8093-7244ORCID · 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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › RNA biology › RNA analysis › RNA bioinformatics › RNA structure prediction
RNA 3D structure prediction |
0.6 | 1 | 2022 | Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
vfold3d · 0.6vfold2d · 0.6VfoldLA · 0.6SHAPE data integration · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Vfold-Pipeline: a web server for RNA 3D structure prediction from sequencesabstractSUMMARY: RNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement. AVAILABILITY AND IMPLEMENTATION: http://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Dong Zhang 0020, Shi-Jie Chen |
Bioinform. | 4 |