Paper Info
Title
Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications.
Abstract
Discriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied.We introduce Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) as an extension of Hidden Conditional Random Fields (HCRFs). GRHCRFs while preserving the discriminative character of HCRFs, can assign labels in agreement with the production rules of a defined grammar. The main GRHCRF novelty is the possibility of including in HCRFs prior knowledge of the problem by means of a defined grammar. Our current implementation allows regular grammar rules. We test our GRHCRF on a typical biosequence labeling problem: the prediction of the topology of Prokaryotic outer-membrane proteins.We show that in a typical biosequence labeling problem the GRHCRF performs better than CRF models of the same complexity, indicating that GRHCRFs can be useful tools for biosequence analysis applications.GRHCRF software is available under GPLv3 licence at the websitehttp://www.biocomp.unibo.it/~savojard/biocrf-0.9.tar.gz.
Year
DOI
Venue
2009
10.1186/1748-7188-4-13
Algorithms for Molecular Biology
Keywords
Field
DocType
algorithms,discriminative model,hidden markov model,bioinformatics,conditional random field
Conditional random field,Rule-based machine translation,Computer science,Grammar,Variable-order Markov model,Generative grammar,Bioinformatics,Hidden Markov model,Discriminative model,Generative model
Journal
Volume
Issue
ISSN
4
1
1748-7188
Citations 
PageRank 
References 
9
0.72
16
Authors
4
Name
Order
Citations
PageRank
Piero Fariselli185196.03
Castrense Savojardo29910.27
Pier Luigi Martelli337529.49
Rita Casadio41032108.10