Demo Data from EDBT 2026 / reviewers in the wild / expert
Kevin Gardner
dblp:88/1626
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0001-8018-4353ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1Applied, 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% | |
| Theoretical computer science
1 paper |
Computational geometry · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › cancer biology
cancer immunology |
0.4 | 1 | 2020 | Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study · SoCG 2020 |
Bioinformatics and computational biology › cancer biology
tumor microenvironment |
0.4 | 1 | 2020 | Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study · SoCG 2020 |
Computational geometry › topological data analysis
persistent homology |
0.1 | 1 | 2020 | Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study · SoCG 2020 |
Computational geometry
topological data analysis |
0.1 | 1 | 2020 | Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study · SoCG 2020 |
Methods — techniques the papers use, named apart from their topics
persistent homology · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study
Andrew Aukerman, Mathieu Carrière, Chao Chen 0012, Kevin Gardner, Raul Rabadan, Rami Vanguri |
SoCG | 4 |
| 2005 | Human promoter genomic composition demonstrates non-random groupings that reflect general cellular functionabstractBACKGROUND: The purpose of this study is to determine whether or not there exists nonrandom grouping of cis-regulatory elements within gene promoters that can be perceived independent of gene expression data and whether or not there is any correlation between this grouping and the biological function of the gene. RESULTS: Using ProSpector, a web-based promoter search and annotation tool, we have applied an unbiased approach to analyze the transcription factor binding site frequencies of 1400 base pair genomic segments positioned at 1200 base pairs upstream and 200 base pairs downstream of the transcriptional start site of 7298 commonly studied human genes. Partitional clustering of the transcription factor binding site composition within these promoter segments reveals a small number of gene groups that are selectively enriched for gene ontology terms consistent with distinct aspects of cellular function. Significance ranking of the class-determining transcription factor binding sites within these clusters show substantial overlap between the gene ontology terms of the transcriptions factors associated with the binding sites and the gene ontology terms of the regulated genes within each group. CONCLUSION: Thus, gene sorting by promoter composition alone produces partitions in which the "regulated" and the "regulators" cosegregate into similar functional classes. These findings demonstrate that the transcription factor binding site composition is non-randomly distributed between gene promoters in a manner that reflects and partially defines general gene class function. Markey C. McNutt, Ron Tongbai, Wenwu Cui, Irene Collins, Wendy J. Freebern, Idalia Montano, Cynthia M. Haggerty, G. V. R. Chandramouli, Kevin Gardner |
BMC Bioinform. | 9 |