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


RESULTS
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.


CONCLUSIONS
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.
Zheng
Bin
B
Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29405, USA. zheng@musc.edu
McLean
David C
DC
Lu
Xinghua
X
D000043:Abstracting and Indexing / Q000379:methods; D000465:Algorithms; D001185:Artificial Intelligence; D016239:MEDLINE; D008954:Models, Biological; D015233:Models, Statistical; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D011506:Proteins / Q000145:classification* / Q000378:metabolism*; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
D011506:Proteins