Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae

Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Cho, Soo Young [verfasserIn] Park, Sung Ho Chai, Jin Choul Lee, Young Seek

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Transcription factor Module network Systems biology Bioinformatics

Anmerkung:	© The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011

Übergeordnetes Werk:	Enthalten in: BioChip journal - Seoul : Soc., 2007, 5(2011), 2 vom: 20. Juni
Übergeordnetes Werk:	volume:5 ; year:2011 ; number:2 ; day:20 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s13206-011-5207-z

Katalog-ID:	SPR030867525

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520			\|a Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs.
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700	1		\|a Lee, Young Seek \|4 aut
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publishDate	2011
allfields	10.1007/s13206-011-5207-z doi (DE-627)SPR030867525 (SPR)s13206-011-5207-z-e DE-627 ger DE-627 rakwb eng Cho, Soo Young verfasserin aut Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011 Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. Transcription factor (dpeaa)DE-He213 Module network (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 Park, Sung Ho aut Chai, Jin Choul aut Lee, Young Seek aut Enthalten in BioChip journal Seoul : Soc., 2007 5(2011), 2 vom: 20. Juni (DE-627)608497258 (DE-600)2513796-7 2092-7843 nnns volume:5 year:2011 number:2 day:20 month:06 https://dx.doi.org/10.1007/s13206-011-5207-z 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2011 2 20 06
spelling	10.1007/s13206-011-5207-z doi (DE-627)SPR030867525 (SPR)s13206-011-5207-z-e DE-627 ger DE-627 rakwb eng Cho, Soo Young verfasserin aut Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011 Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. Transcription factor (dpeaa)DE-He213 Module network (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 Park, Sung Ho aut Chai, Jin Choul aut Lee, Young Seek aut Enthalten in BioChip journal Seoul : Soc., 2007 5(2011), 2 vom: 20. Juni (DE-627)608497258 (DE-600)2513796-7 2092-7843 nnns volume:5 year:2011 number:2 day:20 month:06 https://dx.doi.org/10.1007/s13206-011-5207-z 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2011 2 20 06
allfields_unstemmed	10.1007/s13206-011-5207-z doi (DE-627)SPR030867525 (SPR)s13206-011-5207-z-e DE-627 ger DE-627 rakwb eng Cho, Soo Young verfasserin aut Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011 Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. Transcription factor (dpeaa)DE-He213 Module network (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 Park, Sung Ho aut Chai, Jin Choul aut Lee, Young Seek aut Enthalten in BioChip journal Seoul : Soc., 2007 5(2011), 2 vom: 20. Juni (DE-627)608497258 (DE-600)2513796-7 2092-7843 nnns volume:5 year:2011 number:2 day:20 month:06 https://dx.doi.org/10.1007/s13206-011-5207-z 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2011 2 20 06
allfieldsGer	10.1007/s13206-011-5207-z doi (DE-627)SPR030867525 (SPR)s13206-011-5207-z-e DE-627 ger DE-627 rakwb eng Cho, Soo Young verfasserin aut Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011 Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. Transcription factor (dpeaa)DE-He213 Module network (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 Park, Sung Ho aut Chai, Jin Choul aut Lee, Young Seek aut Enthalten in BioChip journal Seoul : Soc., 2007 5(2011), 2 vom: 20. Juni (DE-627)608497258 (DE-600)2513796-7 2092-7843 nnns volume:5 year:2011 number:2 day:20 month:06 https://dx.doi.org/10.1007/s13206-011-5207-z 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2011 2 20 06
allfieldsSound	10.1007/s13206-011-5207-z doi (DE-627)SPR030867525 (SPR)s13206-011-5207-z-e DE-627 ger DE-627 rakwb eng Cho, Soo Young verfasserin aut Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011 Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. Transcription factor (dpeaa)DE-He213 Module network (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213 Park, Sung Ho aut Chai, Jin Choul aut Lee, Young Seek aut Enthalten in BioChip journal Seoul : Soc., 2007 5(2011), 2 vom: 20. Juni (DE-627)608497258 (DE-600)2513796-7 2092-7843 nnns volume:5 year:2011 number:2 day:20 month:06 https://dx.doi.org/10.1007/s13206-011-5207-z 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2011 2 20 06
language	English
source	Enthalten in BioChip journal 5(2011), 2 vom: 20. Juni volume:5 year:2011 number:2 day:20 month:06
sourceStr	Enthalten in BioChip journal 5(2011), 2 vom: 20. Juni volume:5 year:2011 number:2 day:20 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Transcription factor Module network Systems biology Bioinformatics
isfreeaccess_bool	false
container_title	BioChip journal
authorswithroles_txt_mv	Cho, Soo Young @@aut@@ Park, Sung Ho @@aut@@ Chai, Jin Choul @@aut@@ Lee, Young Seek @@aut@@
publishDateDaySort_date	2011-06-20T00:00:00Z
hierarchy_top_id	608497258
id	SPR030867525
language_de	englisch
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author	Cho, Soo Young
spellingShingle	Cho, Soo Young misc Transcription factor misc Module network misc Systems biology misc Bioinformatics Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae
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topic_title	Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae Transcription factor (dpeaa)DE-He213 Module network (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Bioinformatics (dpeaa)DE-He213
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title	Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae
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title_full	Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae
author_sort	Cho, Soo Young
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author_browse	Cho, Soo Young Park, Sung Ho Chai, Jin Choul Lee, Young Seek
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title_sort	knowledge based construction of functional modules for genetic network in saccharomyces cerevisiae
title_auth	Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae
abstract	Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011
abstractGer	Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011
abstract_unstemmed	Abstract Methods of module prediction typically involve clustering of group genes and the respective transcription factors. These methods are based on the hypothesis that a gene is regulated by a transcription factor when the expression patterns of the gene and the transcription factor are similar. However, this method is not able to predict the direct target of a transcription factor or its effects on the gene expression. In this study, we propose a new approach that uses data integration in order to predict transcription mechanisms, i.e. the targets of transcription factors and the effects on gene expression. We analyzed yeast ChIPchip data, and DNA microarray data obtained under various physiological conditions. We predicted the functional classification of unknown genes using a Support Vector Machine. We validated our results by comparing with other module prediction programs, and found that our module prediction method shows a higher accuracy than others module prediction programs. © The Korean BioChip Society and Springer-Verlag Berlin Heidelberg 2011
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title_short	Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae
url	https://dx.doi.org/10.1007/s13206-011-5207-z
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author2	Park, Sung Ho Chai, Jin Choul Lee, Young Seek
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up_date	2024-07-03T20:36:04.440Z
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Knowledge based construction of functional modules for genetic network in Saccharomyces Cerevisiae

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