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Qiang Fu 0009

dblp:17/1352-9 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › data integration
cross-platform data integration
0.212014
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
YearPublicationVenuePosition
2014 MAGIC: access portal to a cross-platform gene expression compendium for maize
abstract
Abstract 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 modules
abstract
BACKGROUND: 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