Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications. | Algorithms for Molecular Biology BioMed Central Research Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications Piero Fariselli Castrense Savojardo Pier Luigi Martelli and Rita Casadio Open Access Address Biocomputing Group University of Bologna via Irnerio 42 40126 Bologna Italy Email Piero Fariselli - Castrense Savojardo - savojard@ Pier Luigi Martelli - gigi@ Rita Casadio - casadio@ Corresponding author Published 22 October 2009 Received 12 June 2009 Algorithms for Molecular Biology 2009 4 13 doi 1748-7188-4-13 Accepted 22 October 2009 This article is available from http content 4 1 13 2009 Fariselli et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract__ Background 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. Results 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
