Demo Data from EDBT 2026 / reviewers in the wild / expert
Sumedh Prasad
dblp:425/4241
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—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 · 67% Medical and health informatics · 33% |
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
cell biology |
0.8 | 1 | 2024 | Automated, image-based quantification of peroxisome characteristics with perox-per-cell · Bioinform. 2024 |
Bioinformatics and computational biology › bioimage informatics › bioimage analysis
microscopy image analysis |
0.8 | 1 | 2024 | Automated, image-based quantification of peroxisome characteristics with perox-per-cell · Bioinform. 2024 |
Medical and health informatics › medical imaging › medical image analysis
quantitative image analysis |
0.8 | 1 | 2024 | Automated, image-based quantification of peroxisome characteristics with perox-per-cell · Bioinform. 2024 |
Methods — techniques the papers use, named apart from their topics
segmentation · 0.8image processing · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Automated, image-based quantification of peroxisome characteristics with perox-per-cellabstractSUMMARY: perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. AVAILABILITY AND IMPLEMENTATION: The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Maxell L. Neal, Nandini Shukla, Fred D. Mast, Jean-Claude Farré, Therese M. Pacio, Katelyn E. Raney-Plourde, Sumedh Prasad, Suresh Subramani, John D. Aitchison |
Bioinform. | 7 |