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PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information
Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest fac...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137743/ https://www.ncbi.nlm.nih.gov/pubmed/37107700 http://dx.doi.org/10.3390/genes14040942 |
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author | Novoa, Jorge Chagoyen, Mónica Benito, Carlos Moreno, F. Javier Pazos, Florencio |
author_facet | Novoa, Jorge Chagoyen, Mónica Benito, Carlos Moreno, F. Javier Pazos, Florencio |
author_sort | Novoa, Jorge |
collection | PubMed |
description | Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest face thousands of entries (articles) that are difficult to process manually. In this work, we present an interactive tool for automatically digesting large sets of PubMed articles: PMIDigest (PubMed IDs digester). The system allows for classification/sorting of articles according to different criteria, including the type of article and different citation-related figures. It also calculates the distribution of MeSH (medical subject headings) terms for categories of interest, providing in a picture of the themes addressed in the set. These MeSH terms are highlighted in the article abstracts in different colors depending on the category. An interactive representation of the interarticle citation network is also presented in order to easily locate article “clusters” related to particular subjects, as well as their corresponding “hub” articles. In addition to PubMed articles, the system can also process a set of Scopus or Web of Science entries. In summary, with this system, the user can have a “bird’s eye view” of a large set of articles and their main thematic tendencies and obtain additional information not evident in a plain list of abstracts. |
format | Online Article Text |
id | pubmed-10137743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101377432023-04-28 PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information Novoa, Jorge Chagoyen, Mónica Benito, Carlos Moreno, F. Javier Pazos, Florencio Genes (Basel) Article Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest face thousands of entries (articles) that are difficult to process manually. In this work, we present an interactive tool for automatically digesting large sets of PubMed articles: PMIDigest (PubMed IDs digester). The system allows for classification/sorting of articles according to different criteria, including the type of article and different citation-related figures. It also calculates the distribution of MeSH (medical subject headings) terms for categories of interest, providing in a picture of the themes addressed in the set. These MeSH terms are highlighted in the article abstracts in different colors depending on the category. An interactive representation of the interarticle citation network is also presented in order to easily locate article “clusters” related to particular subjects, as well as their corresponding “hub” articles. In addition to PubMed articles, the system can also process a set of Scopus or Web of Science entries. In summary, with this system, the user can have a “bird’s eye view” of a large set of articles and their main thematic tendencies and obtain additional information not evident in a plain list of abstracts. MDPI 2023-04-19 /pmc/articles/PMC10137743/ /pubmed/37107700 http://dx.doi.org/10.3390/genes14040942 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Novoa, Jorge Chagoyen, Mónica Benito, Carlos Moreno, F. Javier Pazos, Florencio PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information |
title | PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information |
title_full | PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information |
title_fullStr | PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information |
title_full_unstemmed | PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information |
title_short | PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information |
title_sort | pmidigest: interactive review of large collections of pubmed entries to distill relevant information |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137743/ https://www.ncbi.nlm.nih.gov/pubmed/37107700 http://dx.doi.org/10.3390/genes14040942 |
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