A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining

Background Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and R...
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Autor*in:	Guo, Mao-Zu [verfasserIn] Li, Jian-Fu Liu, Yang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Evolutionary Tree Neighbor Join Maximum Likelihood Algorithm Local Topological Topological Transformation

Anmerkung:	© Guo et al; licensee BioMed Central Ltd. 2008. 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, 9(2008), Suppl 6 vom: 28. Mai
Übergeordnetes Werk:	volume:9 ; year:2008 ; number:Suppl 6 ; day:28 ; month:05

Links:	Volltext

DOI / URN:	10.1186/1471-2105-9-S6-S4

Katalog-ID:	SPR026849151

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520			\|a Background Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time.
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allfields	10.1186/1471-2105-9-S6-S4 doi (DE-627)SPR026849151 (SPR)1471-2105-9-S6-S4-e DE-627 ger DE-627 rakwb eng Guo, Mao-Zu verfasserin aut A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. Evolutionary Tree (dpeaa)DE-He213 Neighbor Join (dpeaa)DE-He213 Maximum Likelihood Algorithm (dpeaa)DE-He213 Local Topological (dpeaa)DE-He213 Topological Transformation (dpeaa)DE-He213 Li, Jian-Fu aut Liu, Yang aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 6 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 6 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-9-S6-S4 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 9 2008 Suppl 6 28 05
spelling	10.1186/1471-2105-9-S6-S4 doi (DE-627)SPR026849151 (SPR)1471-2105-9-S6-S4-e DE-627 ger DE-627 rakwb eng Guo, Mao-Zu verfasserin aut A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. Evolutionary Tree (dpeaa)DE-He213 Neighbor Join (dpeaa)DE-He213 Maximum Likelihood Algorithm (dpeaa)DE-He213 Local Topological (dpeaa)DE-He213 Topological Transformation (dpeaa)DE-He213 Li, Jian-Fu aut Liu, Yang aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 6 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 6 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-9-S6-S4 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 9 2008 Suppl 6 28 05
allfields_unstemmed	10.1186/1471-2105-9-S6-S4 doi (DE-627)SPR026849151 (SPR)1471-2105-9-S6-S4-e DE-627 ger DE-627 rakwb eng Guo, Mao-Zu verfasserin aut A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. Evolutionary Tree (dpeaa)DE-He213 Neighbor Join (dpeaa)DE-He213 Maximum Likelihood Algorithm (dpeaa)DE-He213 Local Topological (dpeaa)DE-He213 Topological Transformation (dpeaa)DE-He213 Li, Jian-Fu aut Liu, Yang aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 6 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 6 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-9-S6-S4 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 9 2008 Suppl 6 28 05
allfieldsGer	10.1186/1471-2105-9-S6-S4 doi (DE-627)SPR026849151 (SPR)1471-2105-9-S6-S4-e DE-627 ger DE-627 rakwb eng Guo, Mao-Zu verfasserin aut A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. Evolutionary Tree (dpeaa)DE-He213 Neighbor Join (dpeaa)DE-He213 Maximum Likelihood Algorithm (dpeaa)DE-He213 Local Topological (dpeaa)DE-He213 Topological Transformation (dpeaa)DE-He213 Li, Jian-Fu aut Liu, Yang aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 6 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 6 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-9-S6-S4 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 9 2008 Suppl 6 28 05
allfieldsSound	10.1186/1471-2105-9-S6-S4 doi (DE-627)SPR026849151 (SPR)1471-2105-9-S6-S4-e DE-627 ger DE-627 rakwb eng Guo, Mao-Zu verfasserin aut A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. Evolutionary Tree (dpeaa)DE-He213 Neighbor Join (dpeaa)DE-He213 Maximum Likelihood Algorithm (dpeaa)DE-He213 Local Topological (dpeaa)DE-He213 Topological Transformation (dpeaa)DE-He213 Li, Jian-Fu aut Liu, Yang aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 6 vom: 28. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 6 day:28 month:05 https://dx.doi.org/10.1186/1471-2105-9-S6-S4 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 9 2008 Suppl 6 28 05
language	English
source	Enthalten in BMC bioinformatics 9(2008), Suppl 6 vom: 28. Mai volume:9 year:2008 number:Suppl 6 day:28 month:05
sourceStr	Enthalten in BMC bioinformatics 9(2008), Suppl 6 vom: 28. Mai volume:9 year:2008 number:Suppl 6 day:28 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Evolutionary Tree Neighbor Join Maximum Likelihood Algorithm Local Topological Topological Transformation
isfreeaccess_bool	false
container_title	BMC bioinformatics
authorswithroles_txt_mv	Guo, Mao-Zu @@aut@@ Li, Jian-Fu @@aut@@ Liu, Yang @@aut@@
publishDateDaySort_date	2008-05-28T00:00:00Z
hierarchy_top_id	326644814
id	SPR026849151
language_de	englisch
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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 (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. 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author	Guo, Mao-Zu
spellingShingle	Guo, Mao-Zu misc Evolutionary Tree misc Neighbor Join misc Maximum Likelihood Algorithm misc Local Topological misc Topological Transformation A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining
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topic_title	A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining Evolutionary Tree (dpeaa)DE-He213 Neighbor Join (dpeaa)DE-He213 Maximum Likelihood Algorithm (dpeaa)DE-He213 Local Topological (dpeaa)DE-He213 Topological Transformation (dpeaa)DE-He213
topic	misc Evolutionary Tree misc Neighbor Join misc Maximum Likelihood Algorithm misc Local Topological misc Topological Transformation
topic_unstemmed	misc Evolutionary Tree misc Neighbor Join misc Maximum Likelihood Algorithm misc Local Topological misc Topological Transformation
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title	A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining
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title_full	A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining
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author_browse	Guo, Mao-Zu Li, Jian-Fu Liu, Yang
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title_sort	topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ecr and neighbor joining
title_auth	A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining
abstract	Background Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. © Guo et al; licensee BioMed Central Ltd. 2008. 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 Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time. © Guo et al; licensee BioMed Central Ltd. 2008. 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 topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining
url	https://dx.doi.org/10.1186/1471-2105-9-S6-S4
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A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining

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