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Sumedh Prasad

dblp:425/4241 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
cell biology
0.812024
Automated, image-based quantification of peroxisome characteristics with perox-per-cell · Bioinform. 2024
Bioinformatics and computational biology › bioimage informatics › bioimage analysis
microscopy image analysis
0.812024
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
0.812024
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
YearPublicationVenuePosition
2024 Automated, image-based quantification of peroxisome characteristics with perox-per-cell
abstract
SUMMARY: 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