Name
Title | ||
|---|---|---|
Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cells. |
Abstract | ||
|---|---|---|
Background Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer
the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there
is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize
the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression
data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in
EPCs.
Results The predictive integration of gene expression data and standardized functional similarity information enabled us to identify
new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional
similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement
and evaluate an integrated prediction approach based on the concept of k-nearest neighbours learning (kNN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures.
The resulting integrated kNN system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating
characteristic curve > 0.8). We also showed that the proposed models can outperform those discovered by standard gene expression
analysis. Furthermore, we report an initial independent in vitro experimental follow-up, which provides additional evidence of the potential validity of the top biosignature.
Conclusion Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression
data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy
to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization
of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker
discovery problems. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1186/1752-0509-5-46 | BMC systems biology |
Keywords | Field | DocType |
computational biology,functional genomics,gene expression profiling,systems biology,algorithms,adult stem cells,bioinformatics,gene expression analysis,prediction model | Adenosine,Endothelial progenitor cell,Biology,Systems biology,Gene expression,Functional genomics,Adult stem cell,Bioinformatics,Progenitor cell,Gene expression profiling | Journal |
Volume | Issue | ISSN |
5 | 1 | 1752-0509 |
Citations | PageRank | References |
8 | 0.43 | 22 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Azuaje | 1 | 23 | 1.75 |
| Haiying Wang | 2 | 1264 | 171.33 |
| Huiru Zheng | 3 | 458 | 74.87 |
| Frédérique Léonard | 4 | 8 | 0.43 |
| Magali Rolland-Turner | 5 | 8 | 0.43 |
| Lu Zhang | 6 | 8 | 0.43 |
| Yvan Devaux | 7 | 26 | 2.48 |
| Daniel Wagner | 8 | 156 | 11.44 |