Publication:
Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis.

dc.contributor.authorPagano-Márquez, Raquel
dc.contributor.authorCórdoba-Caballero, José
dc.contributor.authorMartínez-Poveda, Beatriz
dc.contributor.authorQuesada, Ana R
dc.contributor.authorRojano, Elena
dc.contributor.authorSeoane, Pedro
dc.contributor.authorRanea, Juan A G
dc.contributor.authorÁngel Medina, Miguel
dc.date.accessioned2023-05-03T13:27:09Z
dc.date.available2023-05-03T13:27:09Z
dc.date.issued2022
dc.description.abstractAngiogenesis is regulated by multiple genes whose variants can lead to different disorders. Among them, rare diseases are a heterogeneous group of pathologies, most of them genetic, whose information may be of interest to determine the still unknown genetic and molecular causes of other diseases. In this work, we use the information on rare diseases dependent on angiogenesis to investigate the genes that are associated with this biological process and to determine if there are interactions between the genes involved in its deregulation. We propose a systemic approach supported by the use of pathological phenotypes to group diseases by semantic similarity. We grouped 158 angiogenesis-related rare diseases in 18 clusters based on their phenotypes. Of them, 16 clusters had traceable gene connections in a high-quality interaction network. These disease clusters are associated with 130 different genes. We searched for genes associated with angiogenesis througth ClinVar pathogenic variants. Of the seven retrieved genes, our system confirms six of them. Furthermore, it allowed us to identify common affected functions among these disease clusters. https://github.com/ElenaRojano/angio_cluster. seoanezonjic@uma.es and elenarojano@uma.es.
dc.identifier.doi10.1093/bib/bbac220
dc.identifier.essn1477-4054
dc.identifier.pmcPMC9294413
dc.identifier.pmid35731990
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294413/pdf
dc.identifier.unpaywallURLhttps://academic.oup.com/bib/article-pdf/23/4/bbac220/45017071/bbac220.pdf
dc.identifier.urihttp://hdl.handle.net/10668/19705
dc.issue.number4
dc.journal.titleBriefings in bioinformatics
dc.journal.titleabbreviationBrief Bioinform
dc.language.isoen
dc.organizationInstituto de Investigación Biomédica de Málaga-IBIMA
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectangiogenesis
dc.subjectdisease clustering
dc.subjectrare diseases
dc.subjectsemantic similarity
dc.subjectsystems biology
dc.subject.meshAlgorithms
dc.subject.meshCluster Analysis
dc.subject.meshComputational Biology
dc.subject.meshHumans
dc.subject.meshPhenotype
dc.subject.meshRare Diseases
dc.subject.meshSemantics
dc.titleDeepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number23
dspace.entity.typePublication

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
PMC9294413.pdf
Size:
2.08 MB
Format:
Adobe Portable Document Format