Demo Data from EDBT 2026 / reviewers in the wild / expert
Jessica Kain
dblp:439/0186
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
genomics |
1.0 | 1 | 2026 | Genomics-informed approach identifies which cell types regulate the metabolome · Bioinform. 2026 |
Bioinformatics and computational biology
single-cell analysis |
1.0 | 1 | 2026 | Genomics-informed approach identifies which cell types regulate the metabolome · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
single-cell RNA sequencing integration · 1.0genome-wide association · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Genomics-informed approach identifies which cell types regulate the metabolomeabstractMOTIVATION: Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear. RESULTS: Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMed and UK Biobank, with one of the most extensive single-cell RNA sequencing resources, Tabula Sapiens. This integration allows us to identify cell types that regulate metabolites body-wide. We find hepatocytes are the primary regulatory cell type for most metabolites, associating with 385/410 (94%) metabolites for whom an association is found. Additionally, our multi-gene approach reveals more metabolite associations with beta cells compared to those identified using a single-gene approach. For example, we identify novel metabolite-cell type associations, such as the association between phenylpropanoic acid and beta cells, this metabolite that was previously thought to be regulated by the microbiome. AVAILABILITY: Code used in this work is available via Github at https://github.com/haimkru/Metabolite-Cell-Type-Associations. Haim Krupkin, Evin M. Padhi, Daniel Nachun, Jessica Kain, Jonathan Z. Long, Stephen B. Montgomery |
Bioinform. | 4 |