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

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

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

Full text

Pdf (288 KB)

Source

Conference on Information and Knowledge Management archive
Proceeding of the 2nd international workshop on Data and text mining in bioinformatics table of contents

Napa Valley, California, USA

SESSION: Short papers table of contents

Pages 77-80

Year of Publication: 2008

ISBN:978-1-60558-251-1

Authors

Tatsuya Asou	Kobe University, Kobe, Japan
Koji Eguchi	Kobe University, Kobe, Japan

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 56, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1458449.1458467 What is a DOI?

ABSTRACT

This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it has not been investigated for such a task. In this paper, we apply the state-of-the-art Collapsed Variational Bayesian Inference and Gibbs Sampling inference to estimating the LDA model, and compared them from the viewpoints of log-likelihoods, classification accuracy and retrieval effectiveness. We demonstrate through experiments that the Collapsed Variational LDA gives better results than the other, especially in terms of classification accuracy and retrieval effectiveness in the task of the protein-protein relationship prediction.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003 [doi>10.1162/jmlr.2003.3.4-5.993]
	2	A. M. Cohenand W. R. Hersh. A survey of current work in biomedical text mining. Briefings in Bioinformatics 6(1):57--71,2005.
	3	T. L. Griffiths and M. Steyvers. Finding scientific topics. Proc. National Academy of Sciences of the United States of America 101:5228--5235, 2004.
	4	David Newman , Chaitanya Chemudugunta , Padhraic Smyth, Statistical entity-topic models, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150487]
	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.