A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins

Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign t...
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Autor*in:	Fariselli, Piero [verfasserIn] Martelli, Pier Luigi Casadio, Rita

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2005

Schlagwörter:	Posterior Probability Hide Markov Model Decode Algorithm Viterbi Algorithm Hide Markov Model Model

Anmerkung:	© Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 6(2005), Suppl 4 vom: 01. Dez.
Übergeordnetes Werk:	volume:6 ; year:2005 ; number:Suppl 4 ; day:01 ; month:12

Links:	Volltext

DOI / URN:	10.1186/1471-2105-6-S4-S12

Katalog-ID:	SPR026827603

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520			\|a Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins.
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allfields	10.1186/1471-2105-6-S4-S12 doi (DE-627)SPR026827603 (SPR)1471-2105-6-S4-S12-e DE-627 ger DE-627 rakwb eng Fariselli, Piero verfasserin aut A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. Posterior Probability (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Decode Algorithm (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Martelli, Pier Luigi aut Casadio, Rita aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 6(2005), Suppl 4 vom: 01. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:6 year:2005 number:Suppl 4 day:01 month:12 https://dx.doi.org/10.1186/1471-2105-6-S4-S12 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2005 Suppl 4 01 12
spelling	10.1186/1471-2105-6-S4-S12 doi (DE-627)SPR026827603 (SPR)1471-2105-6-S4-S12-e DE-627 ger DE-627 rakwb eng Fariselli, Piero verfasserin aut A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. Posterior Probability (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Decode Algorithm (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Martelli, Pier Luigi aut Casadio, Rita aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 6(2005), Suppl 4 vom: 01. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:6 year:2005 number:Suppl 4 day:01 month:12 https://dx.doi.org/10.1186/1471-2105-6-S4-S12 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2005 Suppl 4 01 12
allfields_unstemmed	10.1186/1471-2105-6-S4-S12 doi (DE-627)SPR026827603 (SPR)1471-2105-6-S4-S12-e DE-627 ger DE-627 rakwb eng Fariselli, Piero verfasserin aut A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. Posterior Probability (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Decode Algorithm (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Martelli, Pier Luigi aut Casadio, Rita aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 6(2005), Suppl 4 vom: 01. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:6 year:2005 number:Suppl 4 day:01 month:12 https://dx.doi.org/10.1186/1471-2105-6-S4-S12 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2005 Suppl 4 01 12
allfieldsGer	10.1186/1471-2105-6-S4-S12 doi (DE-627)SPR026827603 (SPR)1471-2105-6-S4-S12-e DE-627 ger DE-627 rakwb eng Fariselli, Piero verfasserin aut A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. Posterior Probability (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Decode Algorithm (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Martelli, Pier Luigi aut Casadio, Rita aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 6(2005), Suppl 4 vom: 01. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:6 year:2005 number:Suppl 4 day:01 month:12 https://dx.doi.org/10.1186/1471-2105-6-S4-S12 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2005 Suppl 4 01 12
allfieldsSound	10.1186/1471-2105-6-S4-S12 doi (DE-627)SPR026827603 (SPR)1471-2105-6-S4-S12-e DE-627 ger DE-627 rakwb eng Fariselli, Piero verfasserin aut A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. Posterior Probability (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Decode Algorithm (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Martelli, Pier Luigi aut Casadio, Rita aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 6(2005), Suppl 4 vom: 01. Dez. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:6 year:2005 number:Suppl 4 day:01 month:12 https://dx.doi.org/10.1186/1471-2105-6-S4-S12 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2005 Suppl 4 01 12
language	English
source	Enthalten in BMC bioinformatics 6(2005), Suppl 4 vom: 01. Dez. volume:6 year:2005 number:Suppl 4 day:01 month:12
sourceStr	Enthalten in BMC bioinformatics 6(2005), Suppl 4 vom: 01. Dez. volume:6 year:2005 number:Suppl 4 day:01 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Posterior Probability Hide Markov Model Decode Algorithm Viterbi Algorithm Hide Markov Model Model
isfreeaccess_bool	false
container_title	BMC bioinformatics
authorswithroles_txt_mv	Fariselli, Piero @@aut@@ Martelli, Pier Luigi @@aut@@ Casadio, Rita @@aut@@
publishDateDaySort_date	2005-12-01T00:00:00Z
hierarchy_top_id	326644814
id	SPR026827603
language_de	englisch
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author	Fariselli, Piero
spellingShingle	Fariselli, Piero misc Posterior Probability misc Hide Markov Model misc Decode Algorithm misc Viterbi Algorithm misc Hide Markov Model Model A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins
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topic_title	A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins Posterior Probability (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Decode Algorithm (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213
topic	misc Posterior Probability misc Hide Markov Model misc Decode Algorithm misc Viterbi Algorithm misc Hide Markov Model Model
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title	A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins
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title_full	A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins
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author_browse	Fariselli, Piero Martelli, Pier Luigi Casadio, Rita
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doi_str_mv	10.1186/1471-2105-6-S4-S12
title_sort	new decoding algorithm for hidden markov models improves the prediction of the topology of all-beta membrane proteins
title_auth	A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins
abstract	Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusion We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins. © Fariselli et al; licensee BioMed Central Ltd 2005. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
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title_short	A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins
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A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins

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