A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data

Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performanc...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Hongkai [verfasserIn] Yuan, Zhongshang Ji, Jiadong Xu, Jing Zhang, Tao Zhang, Xiaoshuai Xue, Fuzhong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Big omics data Markov Blanket-based repeated-fishing strategy (MBRFS) Phenotype-related biomarkers

Anmerkung:	© Li et al. 2016

Übergeordnetes Werk:	Enthalten in: BMC genetics - London : BioMed Central, 2000, 17(2016), 1 vom: 09. März
Übergeordnetes Werk:	volume:17 ; year:2016 ; number:1 ; day:09 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s12863-016-0358-5

Katalog-ID:	SPR027014851

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245	1	2	\|a A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
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520			\|a Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones.
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650		4	\|a Markov Blanket-based repeated-fishing strategy (MBRFS) \|7 (dpeaa)DE-He213
650		4	\|a Phenotype-related biomarkers \|7 (dpeaa)DE-He213
700	1		\|a Yuan, Zhongshang \|4 aut
700	1		\|a Ji, Jiadong \|4 aut
700	1		\|a Xu, Jing \|4 aut
700	1		\|a Zhang, Tao \|4 aut
700	1		\|a Zhang, Xiaoshuai \|4 aut
700	1		\|a Xue, Fuzhong \|4 aut
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author_variant	h l hl z y zy j j jj j x jx t z tz x z xz f x fx
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allfields	10.1186/s12863-016-0358-5 doi (DE-627)SPR027014851 (SPR)s12863-016-0358-5-e DE-627 ger DE-627 rakwb eng Li, Hongkai verfasserin aut A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al. 2016 Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. Big omics data (dpeaa)DE-He213 Markov Blanket-based repeated-fishing strategy (MBRFS) (dpeaa)DE-He213 Phenotype-related biomarkers (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Xu, Jing aut Zhang, Tao aut Zhang, Xiaoshuai aut Xue, Fuzhong aut Enthalten in BMC genetics London : BioMed Central, 2000 17(2016), 1 vom: 09. März (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:17 year:2016 number:1 day:09 month:03 https://dx.doi.org/10.1186/s12863-016-0358-5 kostenfrei 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_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 17 2016 1 09 03
spelling	10.1186/s12863-016-0358-5 doi (DE-627)SPR027014851 (SPR)s12863-016-0358-5-e DE-627 ger DE-627 rakwb eng Li, Hongkai verfasserin aut A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al. 2016 Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. Big omics data (dpeaa)DE-He213 Markov Blanket-based repeated-fishing strategy (MBRFS) (dpeaa)DE-He213 Phenotype-related biomarkers (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Xu, Jing aut Zhang, Tao aut Zhang, Xiaoshuai aut Xue, Fuzhong aut Enthalten in BMC genetics London : BioMed Central, 2000 17(2016), 1 vom: 09. März (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:17 year:2016 number:1 day:09 month:03 https://dx.doi.org/10.1186/s12863-016-0358-5 kostenfrei 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_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 17 2016 1 09 03
allfields_unstemmed	10.1186/s12863-016-0358-5 doi (DE-627)SPR027014851 (SPR)s12863-016-0358-5-e DE-627 ger DE-627 rakwb eng Li, Hongkai verfasserin aut A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al. 2016 Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. Big omics data (dpeaa)DE-He213 Markov Blanket-based repeated-fishing strategy (MBRFS) (dpeaa)DE-He213 Phenotype-related biomarkers (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Xu, Jing aut Zhang, Tao aut Zhang, Xiaoshuai aut Xue, Fuzhong aut Enthalten in BMC genetics London : BioMed Central, 2000 17(2016), 1 vom: 09. März (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:17 year:2016 number:1 day:09 month:03 https://dx.doi.org/10.1186/s12863-016-0358-5 kostenfrei 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_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 17 2016 1 09 03
allfieldsGer	10.1186/s12863-016-0358-5 doi (DE-627)SPR027014851 (SPR)s12863-016-0358-5-e DE-627 ger DE-627 rakwb eng Li, Hongkai verfasserin aut A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al. 2016 Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. Big omics data (dpeaa)DE-He213 Markov Blanket-based repeated-fishing strategy (MBRFS) (dpeaa)DE-He213 Phenotype-related biomarkers (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Xu, Jing aut Zhang, Tao aut Zhang, Xiaoshuai aut Xue, Fuzhong aut Enthalten in BMC genetics London : BioMed Central, 2000 17(2016), 1 vom: 09. März (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:17 year:2016 number:1 day:09 month:03 https://dx.doi.org/10.1186/s12863-016-0358-5 kostenfrei 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_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 17 2016 1 09 03
allfieldsSound	10.1186/s12863-016-0358-5 doi (DE-627)SPR027014851 (SPR)s12863-016-0358-5-e DE-627 ger DE-627 rakwb eng Li, Hongkai verfasserin aut A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Li et al. 2016 Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. Big omics data (dpeaa)DE-He213 Markov Blanket-based repeated-fishing strategy (MBRFS) (dpeaa)DE-He213 Phenotype-related biomarkers (dpeaa)DE-He213 Yuan, Zhongshang aut Ji, Jiadong aut Xu, Jing aut Zhang, Tao aut Zhang, Xiaoshuai aut Xue, Fuzhong aut Enthalten in BMC genetics London : BioMed Central, 2000 17(2016), 1 vom: 09. März (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:17 year:2016 number:1 day:09 month:03 https://dx.doi.org/10.1186/s12863-016-0358-5 kostenfrei 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_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 17 2016 1 09 03
language	English
source	Enthalten in BMC genetics 17(2016), 1 vom: 09. März volume:17 year:2016 number:1 day:09 month:03
sourceStr	Enthalten in BMC genetics 17(2016), 1 vom: 09. März volume:17 year:2016 number:1 day:09 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Big omics data Markov Blanket-based repeated-fishing strategy (MBRFS) Phenotype-related biomarkers
isfreeaccess_bool	true
container_title	BMC genetics
authorswithroles_txt_mv	Li, Hongkai @@aut@@ Yuan, Zhongshang @@aut@@ Ji, Jiadong @@aut@@ Xu, Jing @@aut@@ Zhang, Tao @@aut@@ Zhang, Xiaoshuai @@aut@@ Xue, Fuzhong @@aut@@
publishDateDaySort_date	2016-03-09T00:00:00Z
hierarchy_top_id	326644938
id	SPR027014851
language_de	englisch
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Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. 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author	Li, Hongkai
spellingShingle	Li, Hongkai misc Big omics data misc Markov Blanket-based repeated-fishing strategy (MBRFS) misc Phenotype-related biomarkers A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
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topic_title	A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data Big omics data (dpeaa)DE-He213 Markov Blanket-based repeated-fishing strategy (MBRFS) (dpeaa)DE-He213 Phenotype-related biomarkers (dpeaa)DE-He213
topic	misc Big omics data misc Markov Blanket-based repeated-fishing strategy (MBRFS) misc Phenotype-related biomarkers
topic_unstemmed	misc Big omics data misc Markov Blanket-based repeated-fishing strategy (MBRFS) misc Phenotype-related biomarkers
topic_browse	misc Big omics data misc Markov Blanket-based repeated-fishing strategy (MBRFS) misc Phenotype-related biomarkers
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title	A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
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title_full	A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
author_sort	Li, Hongkai
journal	BMC genetics
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author_browse	Li, Hongkai Yuan, Zhongshang Ji, Jiadong Xu, Jing Zhang, Tao Zhang, Xiaoshuai Xue, Fuzhong
container_volume	17
format_se	Elektronische Aufsätze
author-letter	Li, Hongkai
doi_str_mv	10.1186/s12863-016-0358-5
title_sort	novel markov blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
title_auth	A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
abstract	Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. © Li et al. 2016
abstractGer	Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. © Li et al. 2016
abstract_unstemmed	Background We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Results Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ2 test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ2 test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Conclusions Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones. © Li et al. 2016
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title_short	A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data
url	https://dx.doi.org/10.1186/s12863-016-0358-5
remote_bool	true
author2	Yuan, Zhongshang Ji, Jiadong Xu, Jing Zhang, Tao Zhang, Xiaoshuai Xue, Fuzhong
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up_date	2024-07-03T23:56:35.029Z
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A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data

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