Demo Data from VLDB 2026 / reviewers in the wild / expert
Qiang Fu 0009
dblp:17/1352-9
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2014
0000-0003-4100-2777ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › data integration
cross-platform data integration |
0.2 | 1 | 2014 | MAGIC: access portal to a cross-platform gene expression compendium for maize · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
probe-to-gene mapping · 0.2data integration · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | MAGIC: access portal to a cross-platform gene expression compendium for maizeabstractAbstract Summary: To facilitate the exploration of publicly available Zea mays expression data, we constructed a maize expression compendium, making use of an integration methodology and a consistent probe to gene mapping based on the 5b.60 sequence release of Z. mays. The compendium is made available through a web portal MAGIC that hosts a variety of analysis tools to easily browse and analyze the data. Our compendium is different from previous initiatives in combining expression values across different experiments by providing a consistent gene annotation across different platforms. Availability and implementation: http://bioinformatics.intec.ugent.be/magic/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. Qiang Fu 0009, Ana Carolina Fierro, Pieter Meysman, Aminael Sánchez-Rodríguez, Klaas Vandepoele, Kathleen Marchal, Kristof Engelen |
Bioinform. | 1 |
| 2009 | ModuleDigger: an itemset mining framework for the detection of cis-regulatory modulesabstractBACKGROUND: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. RESULTS: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. CONCLUSION: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input. Hong Sun 0002, Tijl De Bie, Valerie Storms, Qiang Fu 0009, Thomas Dhollander, Karen Lemmens, Annemieke Verstuyf, Bart De Moor, Kathleen Marchal |
BMC Bioinform. | 4 |