Demo Data from EDBT 2026 / reviewers in the wild / expert
Yun-Xing Wang
dblp:141/4704
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene regulation › gene regulatory network
gene regulatory module identification |
0.2 | 1 | 2014 | Identification of transcription factors for drug-associated gene modules and biomedical implications · Bioinform. 2014 |
Bioinformatics and computational biology
systems biology |
0.2 | 1 | 2014 | Identification of transcription factors for drug-associated gene modules and biomedical implications · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
factor analysis · 0.2biclustering · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Identification of transcription factors for drug-associated gene modules and biomedical implicationsabstractMOTIVATION: One of the major findings in systems biomedicine is that both pathogenesis of diseases and drug mode of action have a module basis. However, the transcription factors (TFs) regulating the modules remain largely unknown. RESULTS: In this study, by using biclustering approach FABIA (factor analysis for bicluster acquisition), we generate 49 modules for gene expression profiles on 1309 agent treatments. These modules are of biological relevance in terms of functional enrichment, drug-drug interactions and 3D proximity in chromatins. By using the information of drug targets (some of which are TFs) and biological regulation, the links between 28 modules and 12 specific TFs, such as estrogen receptors (ERs), nuclear factor-like 2 and peroxisome proliferator-activated receptor gamma, can be established. Some of the links are supported by 3D transcriptional regulation data [derived from ChIA-PET (chromatin interaction analysis using paired-end tags) experiments] and drug mode of action as well. The relationships between modules and TFs provide new clues to interpreting biological regulation mechanisms, in particular, the lipid metabolism regulation by ERα. In addition, the links between natural products (e.g. polyphenols) and their associated modules and TFs are helpful to elucidate their polypharmacological effects in terms of activating specific TFs, such as ERs, nuclear factor-like 2 and peroxisome proliferator-activated receptor gamma. Qiang Zhu 0010, Yun-Xing Wang |
Bioinform. | 4 |