Identification of Novel Type III Effectors Using Latent Dirichlet Allocation
Author
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-09-11
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Among the six secretion systems identified in Gram-negative bacteria, the type III secretion system (T3SS) plays important roles in the disease development of pathogens.
T3SS has attracted a great deal of research interests.
However, the secretion mechanism has not been fully understood yet.
Especially, the identification of effectors (secreted proteins) is an important and challenging task.
This paper adopts machine learning methods to identify type III secreted effectors (T3SEs).
We extract features from amino acid sequences and conduct feature reduction based on latent semantic information by using latent Dirichlet allocation model.
The experimental results on Pseudomonas syringae data set demonstrate the good performance of the new methods.
American Psychological Association (APA)
Yang, Yang. 2012. Identification of Novel Type III Effectors Using Latent Dirichlet Allocation. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-6.
https://search.emarefa.net/detail/BIM-491335
Modern Language Association (MLA)
Yang, Yang. Identification of Novel Type III Effectors Using Latent Dirichlet Allocation. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-6.
https://search.emarefa.net/detail/BIM-491335
American Medical Association (AMA)
Yang, Yang. Identification of Novel Type III Effectors Using Latent Dirichlet Allocation. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-6.
https://search.emarefa.net/detail/BIM-491335
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-491335