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...

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Vydáno v:	Genes Ročník 14; číslo 4; s. 942
Hlavní autoři:	Novoa, Jorge, Chagoyen, Mónica, Benito, Carlos, Moreno, F. Javier, Pazos, Florencio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 19.04.2023 MDPI
Témata:	Bibliometrics Breast cancer Citations color Databases, Factual Datasets Documentation face genes Humans Keywords Microorganisms porous media PubMed Visualization literature digest citation databases data mining scientific literature
ISSN:	2073-4425, 2073-4425
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Moreno, F. Javier Benito, Carlos Chagoyen, Mónica Novoa, Jorge Pazos, Florencio
AuthorAffiliation	1 Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), Darwin, 3, 28049 Madrid, Spain 3 Instituto de Investigación en Ciencias de la Alimentación (CIAL), CSIC-UAM, CEI (UAM+CSIC), Nicolás Cabrera, 9, 28049 Madrid, Spain 2 Instituto de Gestión de la Innovación y del Conocimiento, INGENIO (CSIC and U. Politécnica de Valencia), Edificio 8E, Cam. de Vera, 46022 Valencia, Spain
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Cites_doi	10.1038/nj7612-457a 10.1001/jama.1994.03510380059038 10.1093/database/baaa062 10.1186/s13643-019-1074-9 10.1002/jrsm.1354 10.1186/s12874-022-01805-4 10.1038/nrg1272 10.1038/s41580-021-00407-0 10.1016/j.jbi.2011.05.009 10.1093/bioinformatics/btv557 10.1093/bioinformatics/bth386 10.1136/bmjopen-2016-012545 10.1186/gb-2005-6-9-r80 10.1186/gb-2008-9-s2-s8 10.1136/bmj.312.7023.71 10.1186/1751-0473-6-15 10.1038/s41597-020-0543-2 10.1186/1471-2105-7-41 10.1137/S003614450342480 10.1093/database/baab015 10.1038/ng0704-664 10.1038/s41586-021-03819-2 10.1007/s00799-017-0214-x
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Copyright	COPYRIGHT 2023 MDPI AG 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 by the authors. 2023
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Title	PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information
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