Demo Data from EDBT 2026 / reviewers in the wild / expert
Jitong Jiang
dblp:340/3343
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
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 › single-cell analysis
cell-cell interaction analysis |
0.6 | 1 | 2022 | CITEdb: a manually curated database of cell-cell interactions in human · Bioinform. 2022 |
Bioinformatics and computational biology › biological database
biological database curation |
0.2 | 1 | 2022 | CITEdb: a manually curated database of cell-cell interactions in human · Bioinform. 2022 |
Bioinformatics and computational biology
single-cell analysis |
0.2 | 1 | 2022 | CITEdb: a manually curated database of cell-cell interactions in human · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
manual curation · 0.6benchmark dataset construction · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | CITEdb: a manually curated database of cell-cell interactions in humanabstractMOTIVATION: The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions. RESULTS: We develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions. AVAILABILITY AND IMPLEMENTATION: CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Nayang Shan, Dongyu Li, Jitong Jiang, Linlin Yan, Jiudong Gao, Xing-Ming Zhao, Lin Hou 0003 |
Bioinform. | 5 |