Demo Data from SIGIR 2026 / reviewers in the wild / expert
Connor T. Jops
dblp:370/5672
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
genome visualization |
0.7 | 1 | 2023 | GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data · Bioinform. 2023 |
Bioinformatics and computational biology › genomics
genomic data analysis |
0.2 | 1 | 2023 | GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data · Bioinform. 2023 |
Bioinformatics and computational biology › genomics › computational genomics
integrative genomics |
0.2 | 1 | 2023 | GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
layout composition · 0.7interactive plotting · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic dataabstractSUMMARY: With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures. AVAILABILITY AND IMPLEMENTATION: The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Daniel D. Vo, Michi E. Kumagai, Connor T. Jops, Michael J. Gandal |
Bioinform. | 4 |