Dataset Information

Identifying biological concepts from a protein-related corpus with a probabilistic topic model.

ABSTRACT: Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE titles and abstracts by applying a probabilistic topic model.The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information.The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.

SUBMITTER: Zheng B

PROVIDER: S-EPMC1420333 | biostudies-literature | 2006 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying biological concepts from a protein-related corpus with a probabilistic topic model.

Zheng Bin B McLean David C DC Lu Xinghua X

BMC bioinformatics 20060208

<h4>Background</h4>Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE titles and abstracts by applying a probabilistic topic model.<h4>Results</h4>The latent Dirichlet allocation (LDA) model was applied to the corpus. Ba ...[more]

PMID: 16466569

Similar Datasets

Project description:BACKGROUND: In this paper we describe our experience of adding globally unique identifiers to the Species 2000 and ITIS Catalogue of Life, an on-line index of organisms which is intended, ultimately, to cover all the world's known species. The scientific species names held in the Catalogue are names that already play an extensive role as terms in the organisation of information about living organisms in bioinformatics and other domains, but the effectiveness of their use is hindered by variation in individuals' opinions and understanding of these terms; indeed, in some cases more than one name will have been used to refer to the same organism. This means that it is desirable to be able to give unique labels to each of these differing concepts within the catalogue and to be able to determine which concepts are being used in other systems, in order that they can be associated with the concepts in the catalogue. Not only is this needed, but it is also necessary to know the relationships between alternative concepts that scientists might have employed, as these determine what can be inferred when data associated with related concepts is being processed. A further complication is that the catalogue itself is evolving as scientific opinion changes due to an increasing understanding of life. RESULTS: We describe how we are using Life Science Identifiers (LSIDs) as globally unique identifiers in the Catalogue of Life, explaining how the mapping to species concepts is performed, how concepts are associated with specific editions of the catalogue, and how the Taxon Concept Schema has been adopted in order to express information about concepts and their relationships. We explore the implications of using globally unique identifiers in order to refer to abstract concepts such as species, which incorporate at least a measure of subjectivity in their definition, in contrast with the more traditional use of such identifiers to refer to more tangible entities, events, documents, observations, etc. CONCLUSIONS: A major reason for adopting identifiers such as LSIDs is to facilitate data integration. We have demonstrated the incorporation of LSIDs into the Catalogue of Life, in a manner consistent with the biodiversity informatics community's conventions for LSID use. The Catalogue of Life is therefore available as a taxonomy of organisms for use within various disciplines, including biomedical research, by software written with an awareness of these conventions.

Dataset Information

Identifying biological concepts from a protein-related corpus with a probabilistic topic model.

Publications

Identifying biological concepts from a protein-related corpus with a probabilistic topic model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets