{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:57:25Z","timestamp":1776362245455,"version":"3.51.2"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"VUGENE"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>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).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad672","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T01:01:12Z","timestamp":1699405272000},"source":"Crossref","is-referenced-by-count":87,"title":["<i>aPEAR<\/i>: an R package for autonomous visualization of pathway enrichment networks"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9353-038X","authenticated-orcid":false,"given":"Ieva","family":"Kerseviciute","sequence":"first","affiliation":[{"name":"VUGENE , K. Donelaitis street , Kaunas, LT-44248, Lithuania"}]},{"given":"Juozas","family":"Gordevicius","sequence":"additional","affiliation":[{"name":"VUGENE , K. Donelaitis street , Kaunas, LT-44248, Lithuania"}]}],"member":"286","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"2023111304010122300_btad672-B1","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A cluster separation measure","volume":"1","author":"Davies","year":"1979","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2023111304010122300_btad672-B2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01969727408546059","article-title":"Well-separated clusters and optimal fuzzy partitions","volume":"4","author":"Dunn","year":"1974","journal-title":"J Cybern"},{"key":"2023111304010122300_btad672-B3","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1093\/bioinformatics\/btz704","article-title":"Spectrum: fast density-aware spectral clustering for single and multi-omic 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