Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jessie Jiang
dblp:365/3985
· 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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › systems biology › model exchange format
systems biology model exchange |
0.7 | 1 | 2023 | SBMLDiagrams: a python package to process and visualize SBML layout and render · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
skia-python rendering · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | SBMLDiagrams: a python package to process and visualize SBML layout and renderabstractSUMMARY: The systems biology markup language (SBML) is an extensible standard format for exchanging biochemical models. One of the extensions for SBML is the SBML Layout and Render package. This allows modelers to describe a biochemical model as a pathway diagram. However, up to now, there has been little support to help users easily add and retrieve such information from SBML. In this application note, we describe a new Python package called SBMLDiagrams. This package allows a user to add a layout and render information or retrieve it using a straightforward Python API. The package uses skia-python to support the rendering of the diagrams, allowing export to commons formats such as PNG or PDF. AVAILABILITY AND IMPLEMENTATION: SBMLDiagrams is publicly available and licensed under the liberal MIT open-source license. The package is available for all major platforms. The source code has been deposited at GitHub (github.com/sys-bio/SBMLDiagrams). Users can install the package using the standard pip installation mechanism: pip install SBMLDiagrams. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Jessie Jiang, Herbert M. Sauro |
Bioinform. | 2 |