Dataset Information

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

ABSTRACT: The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds. We obtained more than 7 million relationships between more than 74 000 terms, and many types of relationships were not available in any other resource. As the terms were obtained from widely used resources and ontologies, the relationships are given using the standard identifiers provided by them and hence can be linked to other data. A web interface allows users to browse these associations, searching for relationships for a set of terms of interests provided as input, such as between a disease and their associated symptoms, underlying molecular processes or affected tissues. The results are presented in an interactive interface where the user can explore the reported relationships in different ways and follow links to other resources. Database URL: https://csbg.cnb.csic.es/CoMentG/.

SUBMITTER: Novoa J

PROVIDER: S-EPMC10986793 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

Novoa Jorge J López-Ibáñez Javier J Chagoyen Mónica M Ranea Juan A G JAG Pazos Florencio F

Database : the journal of biological databases and curation 20240401

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria ...[more]

PMID: 38564426

Similar Datasets

Project description:BackgroundThere is a growing need for efficient and integrated access to databases provided by diverse institutions. Using a linked data design pattern allows the diverse data on the Internet to be linked effectively and accessed efficiently by computers. Previously, we developed the Allie database, which stores pairs of abbreviations and long forms (LFs, or expanded forms) used in the life sciences. LFs define the semantics of abbreviations, and Allie provides a Web-based search service for researchers to look up the LF of an unfamiliar abbreviation. This service encounters two problems. First, it does not display each LF's definition, which could help the user to disambiguate and learn the abbreviations more easily. Furthermore, there are too many LFs for us to prepare a full dictionary from scratch. On the other hand, DBpedia has made the contents of Wikipedia available in the Resource Description Framework (RDF), which is expected to contain a significant number of entries corresponding to LFs. Therefore, linking the Allie LFs to DBpedia entries may present a solution to the Allie's problems. This requires a method that is capable of matching large numbers of string pairs within a reasonable period of time because Allie and DBpedia are frequently updated.ResultsWe built a Linked Open Data set that links LFs to DBpedia titles by applying key collision methods (i.e., fingerprint and n-gram fingerprint) to their literals, which are simple approximate string-matching methods. In addition, we used UMLS resources to normalise the life science terms. As a result, combining the key collision methods with the domain-specific resources performed best, and 44,027 LFs have links to DBpedia titles. We manually evaluated the accuracy of the string matching by randomly sampling 1200 LFs, and our approach achieved an F-measure of 0.98. In addition, our experiments revealed the following. (1) Performances were similar independently from the frequency of the LFs in MEDLINE. (2) There is a relationship (r2 = 0.96, P < 0.01) between the occurrence frequencies of LFs in MEDLINE and their presence probabilities in DBpedia titles.ConclusionsThe obtained results help Allie users locate the correct LFs. Because the methods are computationally simple and yield a high performance and because the most frequently used LFs in MEDLINE appear more often in DBpedia titles, we can continually and reasonably update the linked dataset to reflect the latest publications and additions to DBpedia. Joining LFs between scientific literature and DBpedia enables cross-resource exploration for mutual benefits.

Dataset Information

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

Publications

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets