Demo Data from EDBT 2026 / reviewers in the wild / expert
Julie A. I. Thoms
dblp:209/7184
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › transcriptomics › non-coding RNA analysis
long noncoding RNA functional annotation |
0.3 | 1 | 2017 | Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based method · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
protein-protein interaction network · 0.3network integration · 0.3miRNA-target association · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based methodabstractMOTIVATION: Long non-coding RNAs (lncRNAs) have been implicated in the regulation of diverse biological functions. The number of newly identified lncRNAs has increased dramatically in recent years but their expression and function have not yet been described from most diseases. To elucidate lncRNA function in human disease, we have developed a novel network based method (NLCFA) integrating correlations between lncRNA, protein coding genes and noncoding miRNAs. We have also integrated target gene associations and protein-protein interactions and designed our model to provide information on the combined influence of mRNAs, lncRNAs and miRNAs on cellular signal transduction networks. RESULTS: We have generated lncRNA expression profiles from the CD34+ haematopoietic stem and progenitor cells (HSPCs) from patients with Myelodysplastic syndromes (MDS) and healthy donors. We report, for the first time, aberrantly expressed lncRNAs in MDS and further prioritize biologically relevant lncRNAs using the NLCFA. Taken together, our data suggests that aberrant levels of specific lncRNAs are intimately involved in network modules that control multiple cancer-associated signalling pathways and cellular processes. Importantly, our method can be applied to prioritize aberrantly expressed lncRNAs for functional validation in other diseases and biological contexts. AVAILABILITY AND IMPLEMENTATION: The method is implemented in R language and Matlab. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Keqin Liu, Dominik Beck, Julie A. I. Thoms, Weiling Zhao, John E. Pimanda, Xiaobo Zhou 0001 |
Bioinform. | 3 |