2026-08-29T17:25:41Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/185662024-09-22T00:10:46Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927
Repisalud author Pagano-Márquez, Raquel author Córdoba-Caballero, José author Martínez-Poveda, Beatriz author Quesada, Ana R author Rojano, Elena author Seoane, Pedro author Ranea, Juan A G author Ángel Medina, Miguel 2024-02-27T14:57:35Z 2024-02-27T14:57:35Z 2022 10.1093/bib/bbac220 1477-4054 Briefings in bioinformatics http://hdl.handle.net/10668/19705 35731990 http://hdl.handle.net/20.500.12105/18566 Angiogenesis 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. eng angiogenesis disease clustering rare diseases semantic similarity systems biology Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis. research article