Network or regression-based methods for disease discrimination: a comparison study

Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-...
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Autor*in:	Zhang, Xiaoshuai [verfasserIn] Yuan, Zhongshang Ji, Jiadong Li, Hongkai Xue, Fuzhong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Disease discrimination AUC Network-based Regression-based

Anmerkung:	© The Author(s). 2016

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 16(2016), 1 vom: 18. Aug.
Übergeordnetes Werk:	volume:16 ; year:2016 ; number:1 ; day:18 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s12874-016-0207-2

Katalog-ID:	SPR027371409

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allfields	10.1186/s12874-016-0207-2 doi (DE-627)SPR027371409 (SPR)s12874-016-0207-2-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoshuai verfasserin aut Network or regression-based methods for disease discrimination: a comparison study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. Disease discrimination (dpeaa)DE-He213 AUC (dpeaa)DE-He213 Network-based (dpeaa)DE-He213 Regression-based (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Li, Hongkai aut Xue, Fuzhong aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 18. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:18 month:08 https://dx.doi.org/10.1186/s12874-016-0207-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 18 08
spelling	10.1186/s12874-016-0207-2 doi (DE-627)SPR027371409 (SPR)s12874-016-0207-2-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoshuai verfasserin aut Network or regression-based methods for disease discrimination: a comparison study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. Disease discrimination (dpeaa)DE-He213 AUC (dpeaa)DE-He213 Network-based (dpeaa)DE-He213 Regression-based (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Li, Hongkai aut Xue, Fuzhong aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 18. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:18 month:08 https://dx.doi.org/10.1186/s12874-016-0207-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 18 08
allfields_unstemmed	10.1186/s12874-016-0207-2 doi (DE-627)SPR027371409 (SPR)s12874-016-0207-2-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoshuai verfasserin aut Network or regression-based methods for disease discrimination: a comparison study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. Disease discrimination (dpeaa)DE-He213 AUC (dpeaa)DE-He213 Network-based (dpeaa)DE-He213 Regression-based (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Li, Hongkai aut Xue, Fuzhong aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 18. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:18 month:08 https://dx.doi.org/10.1186/s12874-016-0207-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 18 08
allfieldsGer	10.1186/s12874-016-0207-2 doi (DE-627)SPR027371409 (SPR)s12874-016-0207-2-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoshuai verfasserin aut Network or regression-based methods for disease discrimination: a comparison study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. Disease discrimination (dpeaa)DE-He213 AUC (dpeaa)DE-He213 Network-based (dpeaa)DE-He213 Regression-based (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Li, Hongkai aut Xue, Fuzhong aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 18. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:18 month:08 https://dx.doi.org/10.1186/s12874-016-0207-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 18 08
allfieldsSound	10.1186/s12874-016-0207-2 doi (DE-627)SPR027371409 (SPR)s12874-016-0207-2-e DE-627 ger DE-627 rakwb eng Zhang, Xiaoshuai verfasserin aut Network or regression-based methods for disease discrimination: a comparison study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. Disease discrimination (dpeaa)DE-He213 AUC (dpeaa)DE-He213 Network-based (dpeaa)DE-He213 Regression-based (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Li, Hongkai aut Xue, Fuzhong aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 16(2016), 1 vom: 18. Aug. (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:16 year:2016 number:1 day:18 month:08 https://dx.doi.org/10.1186/s12874-016-0207-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2016 1 18 08
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spellingShingle	Zhang, Xiaoshuai misc Disease discrimination misc AUC misc Network-based misc Regression-based Network or regression-based methods for disease discrimination: a comparison study
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topic_title	Network or regression-based methods for disease discrimination: a comparison study Disease discrimination (dpeaa)DE-He213 AUC (dpeaa)DE-He213 Network-based (dpeaa)DE-He213 Regression-based (dpeaa)DE-He213
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title	Network or regression-based methods for disease discrimination: a comparison study
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title_full	Network or regression-based methods for disease discrimination: a comparison study
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author_browse	Zhang, Xiaoshuai Yuan, Zhongshang Ji, Jiadong Li, Hongkai Xue, Fuzhong
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title_sort	network or regression-based methods for disease discrimination: a comparison study
title_auth	Network or regression-based methods for disease discrimination: a comparison study
abstract	Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. © The Author(s). 2016
abstractGer	Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. © The Author(s). 2016
abstract_unstemmed	Background In stark contrast to network-centric view for complex disease, regression-based methods are preferred in disease prediction, especially for epidemiologists and clinical professionals. It remains a controversy whether the network-based methods have advantageous performance than regression-based methods, and to what extent do they outperform. Methods Simulations under different scenarios (the input variables are independent or in network relationship) as well as an application were conducted to assess the prediction performance of four typical methods including Bayesian network, neural network, logistic regression and regression splines. Results The simulation results reveal that Bayesian network showed a better performance when the variables were in a network relationship or in a chain structure. For the special wheel network structure, logistic regression had a considerable performance compared to others. Further application on GWAS of leprosy show Bayesian network still outperforms other methods. Conclusion Although regression-based methods are still popular and widely used, network-based approaches should be paid more attention, since they capture the complex relationship between variables. © The Author(s). 2016
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title_short	Network or regression-based methods for disease discrimination: a comparison study
url	https://dx.doi.org/10.1186/s12874-016-0207-2
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author2	Yuan, Zhongshang Ji, Jiadong Li, Hongkai Xue, Fuzhong
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Network or regression-based methods for disease discrimination: a comparison study

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