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Ieva Kerseviciute

dblp:370/4588 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-9353-038XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
pathway enrichment analysis
0.712023
aPEAR: an R package for autonomous visualization of pathway enrichment networks · Bioinform. 2023
Visualization and visual analytics › biological data visualization
biological network visualization
0.712023
aPEAR: an R package for autonomous visualization of pathway enrichment networks · Bioinform. 2023

Methods — techniques the papers use, named apart from their topics

network clustering · 1.3gene set similarity · 1.3
YearPublicationVenuePosition
2023 aPEAR: an R package for autonomous visualization of pathway enrichment networks
abstract
SUMMARY: The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological themes in the experiment. Our approach enables an automated and objective overview of the data without manual and time-consuming parameter tweaking. AVAILABILITY AND IMPLEMENTATION: The package aPEAR is implemented in R, published under the MIT open-source licence. The source code, documentation, and usage instructions are available on https://gitlab.com/vugene/aPEAR as well as on CRAN (https://CRAN.R-project.org/package=aPEAR).
Ieva Kerseviciute, Juozas Gordevicius
Bioinform.1