Demo Data from EDBT 2026 / reviewers in the wild / expert
Tim E. Hardingham
dblp:192/4532
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2
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
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
transcriptomics |
0.4 | 1 | 2019 | SkeletalVis: an exploration and meta-analysis data portal of cross-species skeletal transcriptomics data · Bioinform. 2019 |
Bioinformatics and computational biology › statistical genetics › genotype-phenotype association
disease association |
0.2 | 1 | 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations · Bioinform. 2016 |
Bioinformatics and computational biology
gene expression analysis |
0.2 | 1 | 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations · Bioinform. 2016 |
Bioinformatics and computational biology › gene expression analysis › gene expression pattern analysis
cross-species expression comparison |
0.1 | 1 | 2019 | SkeletalVis: an exploration and meta-analysis data portal of cross-species skeletal transcriptomics data · Bioinform. 2019 |
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction network |
0.1 | 1 | 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
meta-analysis · 0.4gene signature comparison · 0.4phenotype-gene association · 0.2network analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | SkeletalVis: an exploration and meta-analysis data portal of cross-species skeletal transcriptomics dataabstractMOTIVATION: Skeletal diseases are prevalent in society, but improved molecular understanding is required to formulate new therapeutic strategies. Large and increasing quantities of available skeletal transcriptomics experiments give the potential for mechanistic insight of both fundamental skeletal biology and skeletal disease. However, no current repository provides access to processed, readily interpretable analysis of this data. To address this, we have developed SkeletalVis, an exploration portal for skeletal gene expression experiments. RESULTS: The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data. It currently hosts 287 analysed experiments with 739 perturbation responses with comprehensive downstream analysis. We demonstrate its utility in identifying both known and novel relationships between skeletal expression signatures. SkeletalVis provides users with a platform to explore the wealth of available expression data, develop consensus signatures and the ability to compare gene signatures from new experiments to the analysed data to facilitate meta-analysis. AVAILABILITY AND IMPLEMENTATION: The SkeletalVis data portal is freely accessible at http://phenome.manchester.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Jamie Soul, Tim E. Hardingham, Ray P. Boot-Handford, Jean-Marc Schwartz |
Bioinform. | 2 |
| 2016 | PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associationsabstractPhenomeScape is a Cytoscape app which provides easy access to the PhenomeExpress algorithm to interpret gene expression data. PhenomeExpress integrates protein interaction networks with known phenotype to gene associations to find active sub-networks enriched in differentially expressed genes. It also incorporates cross-species phenotypes and associations to include results from animal models of disease. With expression data imported into PhenomeScape, the user can quickly generate and visualise interactive sub-networks. PhenomeScape thus enables researchers to use prior knowledge of a disease to identify differentially regulated sub-networks and to generate an overview of altered biologically processes specific to that disease. AVAILABILITY AND IMPLEMENTATION: Freely available for download at https://github.com/soulj/PhenomeScape CONTACT: [email protected] or [email protected]. Jamie Soul, Sara L. Dunn, Tim E. Hardingham, Ray P. Boot-Handford, Jean-Marc Schwartz |
Bioinform. | 3 |