Predicting protein-protein relationships from literature using collapsed variational latent dirichlet allocation

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 77-80

  Asou, Tatsuya ^a   Eguchi, Koji ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

Biomedical text mining; Probabilistic topic models

Indexed keywords

BIOLOGICAL ENTITIES; BIOMEDICAL TEXT MINING; CLASSIFICATION ACCURACY; GIBBS SAMPLING; LATENT DIRICHLET ALLOCATION; LATENT DIRICHLET ALLOCATIONS; PROBABILISTIC TOPIC MODELS; RETRIEVAL EFFECTIVENESS; TOPIC MODEL; VARIATIONAL BAYESIAN;

BAYESIAN NETWORKS; BIOINFORMATICS; INFERENCE ENGINES; KNOWLEDGE MANAGEMENT; MINING;

MODELS;

EID: 70349235871     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458449.1458467     Document Type: Conference Paper

References (5)

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4. D. Newman, C. Chemudugunta, P. Smyth, and M. Steyvers. Statistical entity-topic models. In Proc. of ACM SIGKDD 2006, pages 680-686, 2006.
5. Y. W. Teh, D. Newman, and M. Welling. A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. Advances in Neural Information Processing Systems, 19:1353-1360, 2007.