메뉴 건너뛰기




Volumn 20, Issue 10, 2015, Pages 664-675

Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Author keywords

[No Author keywords available]

Indexed keywords

GENE EXPRESSION; GENE REGULATORY NETWORK; GENETICS; PHENOTYPE; PLANT; SYSTEMS BIOLOGY;

EID: 84943375424     PISSN: 13601385     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tplants.2015.06.013     Document Type: Review
Times cited : (64)

References (101)
  • 1
    • 13444304325 scopus 로고    scopus 로고
    • NCBI GEO: mining millions of expression profiles - database and tools
    • Barrett T., et al. NCBI GEO: mining millions of expression profiles - database and tools. Nucleic Acids Res. 2005, 33:D562-D566.
    • (2005) Nucleic Acids Res. , vol.33 , pp. D562-D566
    • Barrett, T.1
  • 2
    • 84861109134 scopus 로고    scopus 로고
    • PLEXdb: gene expression resources for plants and plant pathogens
    • Dash S., et al. PLEXdb: gene expression resources for plants and plant pathogens. Nucleic Acids Res. 2012, 40:D1194-D1201.
    • (2012) Nucleic Acids Res. , vol.40 , pp. D1194-D1201
    • Dash, S.1
  • 3
    • 0037249498 scopus 로고    scopus 로고
    • The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community
    • Rhee S.Y., et al. The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. Nucleic Acids Res. 2003, 31:224-228.
    • (2003) Nucleic Acids Res. , vol.31 , pp. 224-228
    • Rhee, S.Y.1
  • 4
    • 84979561805 scopus 로고    scopus 로고
    • Reconstruction of gene regulatory network related to photosynthesis in Arabidopsis thaliana
    • Yu X., et al. Reconstruction of gene regulatory network related to photosynthesis in Arabidopsis thaliana. Front. Plant Sci. 2014, 5:273.
    • (2014) Front. Plant Sci. , vol.5 , pp. 273
    • Yu, X.1
  • 5
    • 84884302416 scopus 로고    scopus 로고
    • Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data
    • Zhu M., et al. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data. BMC Bioinformatics 2013, 14:278.
    • (2013) BMC Bioinformatics , vol.14 , pp. 278
    • Zhu, M.1
  • 6
    • 79959350055 scopus 로고    scopus 로고
    • Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions
    • Bassel G.W., et al. Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions. Proc. Natl. Acad. Sci. U.S.A. 2011, 108:9709-9714.
    • (2011) Proc. Natl. Acad. Sci. U.S.A. , vol.108 , pp. 9709-9714
    • Bassel, G.W.1
  • 7
    • 71749094192 scopus 로고    scopus 로고
    • Arabidopsis gene co-expression network and its functional modules
    • Mao L.Y., et al. Arabidopsis gene co-expression network and its functional modules. BMC Bioinformatics 2009, 10:346.
    • (2009) BMC Bioinformatics , vol.10 , pp. 346
    • Mao, L.Y.1
  • 8
    • 79951574076 scopus 로고    scopus 로고
    • OryzaExpress: an integrated database of gene expression networks and omics annotations in rice
    • Hamada K., et al. OryzaExpress: an integrated database of gene expression networks and omics annotations in rice. Plant Cell Physiol. 2011, 52:220-229.
    • (2011) Plant Cell Physiol. , vol.52 , pp. 220-229
    • Hamada, K.1
  • 9
    • 84892736334 scopus 로고    scopus 로고
    • ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants
    • Obayashi T., et al. ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants. Plant Cell Physiol. 2014, 55:e6.
    • (2014) Plant Cell Physiol. , vol.55 , pp. e6
    • Obayashi, T.1
  • 10
    • 79955581503 scopus 로고    scopus 로고
    • PlaNet: combined sequence and expression comparisons across plant networks derived from seven species
    • Mutwil M., et al. PlaNet: combined sequence and expression comparisons across plant networks derived from seven species. Plant Cell 2011, 23:895-910.
    • (2011) Plant Cell , vol.23 , pp. 895-910
    • Mutwil, M.1
  • 11
    • 0036947892 scopus 로고    scopus 로고
    • Robust estimators for expression analysis
    • Hubbell E., et al. Robust estimators for expression analysis. Bioinformatics 2002, 18:1585-1592.
    • (2002) Bioinformatics , vol.18 , pp. 1585-1592
    • Hubbell, E.1
  • 12
    • 0142121516 scopus 로고    scopus 로고
    • Exploration, normalization, and summaries of high density oligonucleotide array probe level data
    • Irizarry R.A., et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003, 4:249-264.
    • (2003) Biostatistics , vol.4 , pp. 249-264
    • Irizarry, R.A.1
  • 13
    • 10844290766 scopus 로고    scopus 로고
    • A model-based background adjustment for oligonucleotide expression arrays
    • Wu Z.J., et al. A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 2004, 99:909-917.
    • (2004) J. Am. Stat. Assoc. , vol.99 , pp. 909-917
    • Wu, Z.J.1
  • 14
    • 0035793042 scopus 로고    scopus 로고
    • Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection
    • Li C., Wong W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. U.S.A. 2001, 98:31-36.
    • (2001) Proc. Natl. Acad. Sci. U.S.A. , vol.98 , pp. 31-36
    • Li, C.1    Wong, W.H.2
  • 15
    • 34547830867 scopus 로고    scopus 로고
    • Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks
    • Lim W.K., et al. Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics 2007, 23:I282-I288.
    • (2007) Bioinformatics , vol.23 , pp. I282-I288
    • Lim, W.K.1
  • 16
    • 77958471357 scopus 로고    scopus 로고
    • Differential expression analysis for sequence count data
    • Anders S., Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010, 11:R106.
    • (2010) Genome Biol. , vol.11 , pp. R106
    • Anders, S.1    Huber, W.2
  • 17
    • 75249087100 scopus 로고    scopus 로고
    • EdgeR: a Bioconductor package for differential expression analysis of digital gene expression data
    • Robinson M.D., et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26:139-140.
    • (2010) Bioinformatics , vol.26 , pp. 139-140
    • Robinson, M.D.1
  • 18
    • 77949481052 scopus 로고    scopus 로고
    • Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
    • Bullard J.H., et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010, 11:94.
    • (2010) BMC Bioinformatics , vol.11 , pp. 94
    • Bullard, J.H.1
  • 19
    • 84887791432 scopus 로고    scopus 로고
    • A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
    • Dillies M.A., et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 2013, 14:671-683.
    • (2013) Brief. Bioinform. , vol.14 , pp. 671-683
    • Dillies, M.A.1
  • 21
    • 84947318637 scopus 로고
    • Locally weighted regression: an approach to regression analysis by local fitting
    • Cleveland W.S., Devlin S.J. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 1988, 83:596-610.
    • (1988) J. Am. Stat. Assoc. , vol.83 , pp. 596-610
    • Cleveland, W.S.1    Devlin, S.J.2
  • 22
    • 84872596110 scopus 로고    scopus 로고
    • Reuse of public genome-wide gene expression data
    • Rung J., Brazma A. Reuse of public genome-wide gene expression data. Nat. Rev. Genet. 2013, 14:89-99.
    • (2013) Nat. Rev. Genet. , vol.14 , pp. 89-99
    • Rung, J.1    Brazma, A.2
  • 23
    • 34548810242 scopus 로고    scopus 로고
    • Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
    • Calza S., et al. Filtering genes to improve sensitivity in oligonucleotide microarray data analysis. Nucleic Acids Res. 2007, 35:e102.
    • (2007) Nucleic Acids Res. , vol.35 , pp. e102
    • Calza, S.1
  • 24
    • 63449142392 scopus 로고    scopus 로고
    • Filtering for increased power for microarray data analysis
    • Hackstadt A.J., Hess A.M. Filtering for increased power for microarray data analysis. BMC Bioinformatics 2009, 10:11.
    • (2009) BMC Bioinformatics , vol.10 , pp. 11
    • Hackstadt, A.J.1    Hess, A.M.2
  • 25
    • 67650527109 scopus 로고    scopus 로고
    • Filtering genes for cluster and network analysis
    • Tritchler D., et al. Filtering genes for cluster and network analysis. BMC Bioinformatics 2009, 10:193.
    • (2009) BMC Bioinformatics , vol.10 , pp. 193
    • Tritchler, D.1
  • 26
    • 80052198668 scopus 로고    scopus 로고
    • Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays
    • Lu J., et al. Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays. Nucleic Acids Res. 2011, 39:e86.
    • (2011) Nucleic Acids Res. , vol.39 , pp. e86
    • Lu, J.1
  • 27
    • 77953095629 scopus 로고    scopus 로고
    • Independent filtering increases detection power for high-throughput experiments
    • Bourgon R., et al. Independent filtering increases detection power for high-throughput experiments. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:9546-9551.
    • (2010) Proc. Natl. Acad. Sci. U.S.A. , vol.107 , pp. 9546-9551
    • Bourgon, R.1
  • 28
    • 70449115789 scopus 로고    scopus 로고
    • Co-expression tools for plant biology: opportunities for hypothesis generation and caveats
    • Usadel B., et al. Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ. 2009, 32:1633-1651.
    • (2009) Plant Cell Environ. , vol.32 , pp. 1633-1651
    • Usadel, B.1
  • 29
    • 33947305781 scopus 로고    scopus 로고
    • ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context
    • Margolin A.A., et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006, 7:S7.
    • (2006) BMC Bioinformatics , vol.7 , pp. S7
    • Margolin, A.A.1
  • 30
    • 84913538927 scopus 로고    scopus 로고
    • A comparative study of statistical methods used to identify dependencies between gene expression signals
    • de Siqueira Santos S., et al. A comparative study of statistical methods used to identify dependencies between gene expression signals. Brief Bioinform 2014, 15:906-918.
    • (2014) Brief Bioinform , vol.15 , pp. 906-918
    • de Siqueira Santos, S.1
  • 31
    • 23744489846 scopus 로고    scopus 로고
    • Simulations of simple artificial genetic networks reveal features in the use of relevance networks
    • Lindlöf A., Lubovac Z. Simulations of simple artificial genetic networks reveal features in the use of relevance networks. In Silico Biol. 2005, 5:239-249.
    • (2005) In Silico Biol. , vol.5 , pp. 239-249
    • Lindlöf, A.1    Lubovac, Z.2
  • 32
    • 50849124115 scopus 로고    scopus 로고
    • Measuring and testing dependence by correlation of distances
    • Székely G.J., et al. Measuring and testing dependence by correlation of distances. Ann. Stat. 2007, 35:2769-2794.
    • (2007) Ann. Stat. , vol.35 , pp. 2769-2794
    • Székely, G.J.1
  • 33
    • 0000861053 scopus 로고
    • A non-parametric test of independence
    • Hoeffding W. A non-parametric test of independence. Ann. Math. Stat. 1948, 19:546-557.
    • (1948) Ann. Math. Stat. , vol.19 , pp. 546-557
    • Hoeffding, W.1
  • 34
    • 84878025952 scopus 로고    scopus 로고
    • A consistent multivariate test of association based on ranks of distances
    • Heller R., et al. A consistent multivariate test of association based on ranks of distances. Biometrika 2013, 100:503-510.
    • (2013) Biometrika , vol.100 , pp. 503-510
    • Heller, R.1
  • 35
    • 84865842920 scopus 로고    scopus 로고
    • Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis
    • Ma C., Wang X.F. Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. Plant Physiol. 2012, 160:192-203.
    • (2012) Plant Physiol. , vol.160 , pp. 192-203
    • Ma, C.1    Wang, X.F.2
  • 36
    • 83755163018 scopus 로고    scopus 로고
    • Detecting novel associations in large data sets
    • Reshef D.N., et al. Detecting novel associations in large data sets. Science 2011, 334:1518-1524.
    • (2011) Science , vol.334 , pp. 1518-1524
    • Reshef, D.N.1
  • 37
    • 84861168271 scopus 로고    scopus 로고
    • Inferring gene regulatory networks by ANOVA
    • Kuffner R., et al. Inferring gene regulatory networks by ANOVA. Bioinformatics 2012, 28:1376-1382.
    • (2012) Bioinformatics , vol.28 , pp. 1376-1382
    • Kuffner, R.1
  • 38
    • 84922391597 scopus 로고    scopus 로고
    • A ratiometric-based measure of gene co-expression
    • Abelin A.C., et al. A ratiometric-based measure of gene co-expression. BMC Bioinformatics 2014, 15:331.
    • (2014) BMC Bioinformatics , vol.15 , pp. 331
    • Abelin, A.C.1
  • 39
    • 12344321571 scopus 로고    scopus 로고
    • Discovery of meaningful associations in genomic data using partial correlation coefficients
    • de la Fuente A., et al. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 2004, 20:3565-3574.
    • (2004) Bioinformatics , vol.20 , pp. 3565-3574
    • de la Fuente, A.1
  • 40
    • 73149097219 scopus 로고    scopus 로고
    • Regularized estimation of large-scale gene association networks using graphical Gaussian models
    • Kramer N., et al. Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics 2009, 10:384.
    • (2009) BMC Bioinformatics , vol.10 , pp. 384
    • Kramer, N.1
  • 41
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the Lasso
    • Meinshausen N., Buhlmann P. High-dimensional graphs and variable selection with the Lasso. Ann. Stat. 2006, 34:1436-1462.
    • (2006) Ann. Stat. , vol.34 , pp. 1436-1462
    • Meinshausen, N.1    Buhlmann, P.2
  • 42
    • 45849134070 scopus 로고    scopus 로고
    • Sparse inverse covariance estimation with the graphical lasso
    • Friedman J., et al. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 2008, 9:432-441.
    • (2008) Biostatistics , vol.9 , pp. 432-441
    • Friedman, J.1
  • 43
    • 0001878035 scopus 로고
    • Multiple regression analysis
    • Wiley, A. Ralston, H.S. Wilf (Eds.)
    • Efroymson M.A. Multiple regression analysis. Mathematical Methods for Digital Computers 1960, 191-203. Wiley. A. Ralston, H.S. Wilf (Eds.).
    • (1960) Mathematical Methods for Digital Computers , pp. 191-203
    • Efroymson, M.A.1
  • 44
    • 3242708140 scopus 로고    scopus 로고
    • Least angle regression
    • Efron B., et al. Least angle regression. Ann. Stat. 2004, 32:407-451.
    • (2004) Ann. Stat. , vol.32 , pp. 407-451
    • Efron, B.1
  • 45
    • 84942484786 scopus 로고
    • Ridge regression: biased estimation for nonorthogonal problems
    • Hoerl A.E., Kennard R.W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970, 12:55-67.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 46
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the Lasso
    • Tibshirani R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 1996, 58:267-288.
    • (1996) J. R. Stat. Soc. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 47
    • 84870305264 scopus 로고    scopus 로고
    • Wisdom of crowds for robust gene network inference
    • Marbach D., et al. Wisdom of crowds for robust gene network inference. Nat. Methods 2012, 9:796-804.
    • (2012) Nat. Methods , vol.9 , pp. 796-804
    • Marbach, D.1
  • 48
    • 84869882656 scopus 로고    scopus 로고
    • TIGRESS: trustful inference of gene regulation using stability selection
    • Haury A.C., et al. TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst. Biol. 2012, 6:145.
    • (2012) BMC Syst. Biol. , vol.6 , pp. 145
    • Haury, A.C.1
  • 49
    • 84899876508 scopus 로고    scopus 로고
    • NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms
    • Ruyssinck J., et al. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms. PLoS ONE 2014, 9:e92709.
    • (2014) PLoS ONE , vol.9 , pp. e92709
    • Ruyssinck, J.1
  • 50
    • 84455173311 scopus 로고    scopus 로고
    • Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis
    • Vignes M., et al. Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis. PLoS ONE 2011, 6:e29165.
    • (2011) PLoS ONE , vol.6 , pp. e29165
    • Vignes, M.1
  • 51
    • 80052699490 scopus 로고    scopus 로고
    • Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression
    • Morrissey E.R., et al. Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression. Biostatistics 2011, 12:682-694.
    • (2011) Biostatistics , vol.12 , pp. 682-694
    • Morrissey, E.R.1
  • 52
    • 4444244398 scopus 로고    scopus 로고
    • Gene interaction network suggests dioxin induces a significant linkage between aryl hydrocarbon receptor and retinoic acid receptor beta
    • Toyoshiba H., et al. Gene interaction network suggests dioxin induces a significant linkage between aryl hydrocarbon receptor and retinoic acid receptor beta. Environ. Health Persp. 2004, 112:1217-1224.
    • (2004) Environ. Health Persp. , vol.112 , pp. 1217-1224
    • Toyoshiba, H.1
  • 53
    • 3042738945 scopus 로고    scopus 로고
    • Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data
    • Kim S., et al. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 2004, 75:57-65.
    • (2004) Biosystems , vol.75 , pp. 57-65
    • Kim, S.1
  • 54
    • 84908274658 scopus 로고    scopus 로고
    • Variable selection for BART: an application to gene regulation
    • Bleich J., et al. Variable selection for BART: an application to gene regulation. Ann. Appl. Stat. 2014, 8:1750-1781.
    • (2014) Ann. Appl. Stat. , vol.8 , pp. 1750-1781
    • Bleich, J.1
  • 55
    • 77958570788 scopus 로고    scopus 로고
    • Inferring regulatory networks from expression data using tree-based methods
    • Huynh-Thu V.A., et al. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 2010, 5:e12776.
    • (2010) PLoS ONE , vol.5 , pp. e12776
    • Huynh-Thu, V.A.1
  • 56
    • 0842288337 scopus 로고    scopus 로고
    • Inferring cellular networks using probabilistic graphical models
    • Friedman N. Inferring cellular networks using probabilistic graphical models. Science 2004, 303:799-805.
    • (2004) Science , vol.303 , pp. 799-805
    • Friedman, N.1
  • 57
    • 33746035971 scopus 로고    scopus 로고
    • The max-min hill-climbing Bayesian network structure learning algorithm
    • Tsamardinos I., et al. The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 2006, 65:31-78.
    • (2006) Mach. Learn. , vol.65 , pp. 31-78
    • Tsamardinos, I.1
  • 58
    • 84887065692 scopus 로고    scopus 로고
    • A novel algorithm for scalable and accurate Bayesian network learning
    • Brown L.E., et al. A novel algorithm for scalable and accurate Bayesian network learning. Stud. Health Technol. Inform. 2004, 107:711-715.
    • (2004) Stud. Health Technol. Inform. , vol.107 , pp. 711-715
    • Brown, L.E.1
  • 59
    • 0001038826 scopus 로고
    • Covariance selection
    • Dempster A.P. Covariance selection. Biometrics 1972, 28:157-175.
    • (1972) Biometrics , vol.28 , pp. 157-175
    • Dempster, A.P.1
  • 61
    • 84911409703 scopus 로고    scopus 로고
    • Ensemble-based network aggregation improves the accuracy of gene network reconstruction
    • Zhong R., et al. Ensemble-based network aggregation improves the accuracy of gene network reconstruction. PLoS ONE 2014, 9:e106319.
    • (2014) PLoS ONE , vol.9 , pp. e106319
    • Zhong, R.1
  • 62
    • 84922623602 scopus 로고    scopus 로고
    • Weighted ensemble learning of Bayesian network for gene regulatory networks
    • Njah H., Jamoussi S. Weighted ensemble learning of Bayesian network for gene regulatory networks. Neurocomputing 2015, 150(Part B):404-416.
    • (2015) Neurocomputing , vol.150 , pp. 404-416
    • Njah, H.1    Jamoussi, S.2
  • 63
    • 84886916597 scopus 로고    scopus 로고
    • ENNET: inferring large gene regulatory networks from expression data using gradient boosting
    • Slawek J., Arodz T. ENNET: inferring large gene regulatory networks from expression data using gradient boosting. BMC Syst. Biol. 2013, 7:106.
    • (2013) BMC Syst. Biol. , vol.7 , pp. 106
    • Slawek, J.1    Arodz, T.2
  • 64
    • 0141993704 scopus 로고    scopus 로고
    • A gene-coexpression network for global discovery of conserved genetic modules
    • Stuart J.M., et al. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003, 302:249-255.
    • (2003) Science , vol.302 , pp. 249-255
    • Stuart, J.M.1
  • 65
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
    • (1996) Mach. Learn. , vol.24 , pp. 123-140
    • Breiman, L.1
  • 66
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Schapire R.E. The strength of weak learnability. Mach. Learn. 1990, 5:197-227.
    • (1990) Mach. Learn. , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 68
    • 76349125292 scopus 로고    scopus 로고
    • Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana
    • Lee I., et al. Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana. Nat. Biotechnol. 2010, 28:149-156.
    • (2010) Nat. Biotechnol. , vol.28 , pp. 149-156
    • Lee, I.1
  • 69
    • 0033669189 scopus 로고    scopus 로고
    • A network of protein-protein interactions in yeast
    • Schwikowski B., et al. A network of protein-protein interactions in yeast. Nat. Biotechnol. 2000, 18:1257-1261.
    • (2000) Nat. Biotechnol. , vol.18 , pp. 1257-1261
    • Schwikowski, B.1
  • 70
    • 0038699587 scopus 로고    scopus 로고
    • Global protein function prediction from protein-protein interaction networks
    • Vazquez A., et al. Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 2003, 21:697-700.
    • (2003) Nat. Biotechnol. , vol.21 , pp. 697-700
    • Vazquez, A.1
  • 71
    • 0037941585 scopus 로고    scopus 로고
    • Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
    • Segal E., et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 2003, 34:166-176.
    • (2003) Nat. Genet. , vol.34 , pp. 166-176
    • Segal, E.1
  • 72
    • 60149110901 scopus 로고    scopus 로고
    • Module networks revisited: computational assessment and prioritization of model predictions
    • Joshi A., et al. Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics 2009, 25:490-496.
    • (2009) Bioinformatics , vol.25 , pp. 490-496
    • Joshi, A.1
  • 73
    • 84903650273 scopus 로고    scopus 로고
    • Floral transcriptomes in woodland strawberry uncover developing receptacle and anther gene networks
    • Hollender C.A., et al. Floral transcriptomes in woodland strawberry uncover developing receptacle and anther gene networks. Plant Physiol. 2014, 165:1062-1075.
    • (2014) Plant Physiol. , vol.165 , pp. 1062-1075
    • Hollender, C.A.1
  • 74
    • 48249134043 scopus 로고    scopus 로고
    • Evolution of evolvability in gene regulatory networks
    • Crombach A., Hogeweg P. Evolution of evolvability in gene regulatory networks. PLoS Comput. Biol. 2008, 4:e1000112.
    • (2008) PLoS Comput. Biol. , vol.4 , pp. e1000112
    • Crombach, A.1    Hogeweg, P.2
  • 75
    • 84861477170 scopus 로고    scopus 로고
    • Identification of gene modules associated with drought response in rice by network-based analysis
    • Zhang L.D., et al. Identification of gene modules associated with drought response in rice by network-based analysis. PLoS ONE 2012, 7:e33748.
    • (2012) PLoS ONE , vol.7 , pp. e33748
    • Zhang, L.D.1
  • 76
    • 33947252154 scopus 로고    scopus 로고
    • Network-based prediction of protein function
    • Sharan R., et al. Network-based prediction of protein function. Mol. Syst. Biol. 2007, 3:88.
    • (2007) Mol. Syst. Biol. , vol.3 , pp. 88
    • Sharan, R.1
  • 77
    • 81055126078 scopus 로고    scopus 로고
    • Genetic dissection of the biotic stress response using a genome-scale gene network for rice
    • Lee I., et al. Genetic dissection of the biotic stress response using a genome-scale gene network for rice. Proc. Natl. Acad. Sci. U.S.A. 2011, 108:18548-18553.
    • (2011) Proc. Natl. Acad. Sci. U.S.A. , vol.108 , pp. 18548-18553
    • Lee, I.1
  • 78
    • 79960657553 scopus 로고    scopus 로고
    • Gene coexpression network analysis as a source of functional annotation for rice genes
    • Childs K.L., et al. Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS ONE 2011, 6:e22196.
    • (2011) PLoS ONE , vol.6 , pp. e22196
    • Childs, K.L.1
  • 79
    • 79959970191 scopus 로고    scopus 로고
    • Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice
    • Movahedi S., et al. Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice. Plant Physiol. 2011, 156:1316-1330.
    • (2011) Plant Physiol. , vol.156 , pp. 1316-1330
    • Movahedi, S.1
  • 80
    • 80051585495 scopus 로고    scopus 로고
    • Comparative physiology and transcriptional networks underlying the heat shock response in Populus trichocarpa, Arabidopsis thaliana and Glycine max
    • Weston D.J., et al. Comparative physiology and transcriptional networks underlying the heat shock response in Populus trichocarpa, Arabidopsis thaliana and Glycine max. Plant Cell Environ. 2011, 34:1488-1506.
    • (2011) Plant Cell Environ. , vol.34 , pp. 1488-1506
    • Weston, D.J.1
  • 81
    • 34548724489 scopus 로고    scopus 로고
    • The evolution of genetic networks by non-adaptive processes
    • Lynch M. The evolution of genetic networks by non-adaptive processes. Nat. Rev. Genet. 2007, 8:803-813.
    • (2007) Nat. Rev. Genet. , vol.8 , pp. 803-813
    • Lynch, M.1
  • 82
    • 84857780876 scopus 로고    scopus 로고
    • Evidence for network evolution in an Arabidopsis interactome map
    • Braun P., et al. Evidence for network evolution in an Arabidopsis interactome map. Science 2011, 333:601-607.
    • (2011) Science , vol.333 , pp. 601-607
    • Braun, P.1
  • 83
    • 84865739425 scopus 로고    scopus 로고
    • Architecture of the human regulatory network derived from ENCODE data
    • Gerstein M.B., et al. Architecture of the human regulatory network derived from ENCODE data. Nature 2012, 489:91-100.
    • (2012) Nature , vol.489 , pp. 91-100
    • Gerstein, M.B.1
  • 84
    • 84885580971 scopus 로고    scopus 로고
    • Incorporating prior knowledge into gene network study
    • Wang Z.X., et al. Incorporating prior knowledge into gene network study. Bioinformatics 2013, 29:2633-2640.
    • (2013) Bioinformatics , vol.29 , pp. 2633-2640
    • Wang, Z.X.1
  • 85
    • 82155203000 scopus 로고    scopus 로고
    • Integration of gene expression data with prior knowledge for network analysis and validation
    • Ante M., et al. Integration of gene expression data with prior knowledge for network analysis and validation. BMC Res. Notes 2011, 4:520.
    • (2011) BMC Res. Notes , vol.4 , pp. 520
    • Ante, M.1
  • 86
    • 80052338881 scopus 로고    scopus 로고
    • Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana
    • Chan E.K.F., et al. Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol. 2011, 9:e1001125.
    • (2011) PLoS Biol. , vol.9 , pp. e1001125
    • Chan, E.K.F.1
  • 87
    • 84926147304 scopus 로고    scopus 로고
    • Selecting causal genes from genome-wide association studies via functionally coherent subnetworks
    • Tasan M., et al. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat. Methods 2015, 12:154-159.
    • (2015) Nat. Methods , vol.12 , pp. 154-159
    • Tasan, M.1
  • 88
    • 84862487776 scopus 로고    scopus 로고
    • Six degrees of epistasis: statistical network models for GWAS
    • McKinney B.A., Pajewski N.M. Six degrees of epistasis: statistical network models for GWAS. Front. Genet. 2011, 2:109.
    • (2011) Front. Genet. , vol.2 , pp. 109
    • McKinney, B.A.1    Pajewski, N.M.2
  • 89
    • 84891397152 scopus 로고    scopus 로고
    • A network-based, integrative approach to identify genes with aberrant co-methylation in colorectal cancer
    • Li Y.S., et al. A network-based, integrative approach to identify genes with aberrant co-methylation in colorectal cancer. Mol. Biosyst. 2014, 10:180-190.
    • (2014) Mol. Biosyst. , vol.10 , pp. 180-190
    • Li, Y.S.1
  • 90
    • 84911362590 scopus 로고    scopus 로고
    • Network-guided regression for detecting associations between DNA methylation and gene expression
    • Wang Z., et al. Network-guided regression for detecting associations between DNA methylation and gene expression. Bioinformatics 2014, 30:2693-2701.
    • (2014) Bioinformatics , vol.30 , pp. 2693-2701
    • Wang, Z.1
  • 91
    • 84932093797 scopus 로고    scopus 로고
    • How difficult is inference of mammalian causal gene regulatory networks?
    • Djordjevic D., et al. How difficult is inference of mammalian causal gene regulatory networks?. PLoS ONE 2014, 9:e111661.
    • (2014) PLoS ONE , vol.9 , pp. e111661
    • Djordjevic, D.1
  • 92
    • 84925679449 scopus 로고    scopus 로고
    • Dissecting meiotic recombination based on tetrad analysis by single-microspore sequencing in maize
    • Li X., et al. Dissecting meiotic recombination based on tetrad analysis by single-microspore sequencing in maize. Nat. Commun. 2015, 6:6648.
    • (2015) Nat. Commun. , vol.6 , pp. 6648
    • Li, X.1
  • 93
    • 84883785822 scopus 로고    scopus 로고
    • Multiplex and homologous recombination-mediated genome editing in Arabidopsis and Nicotiana benthamiana using guide RNA and Cas9
    • Li J.F., et al. Multiplex and homologous recombination-mediated genome editing in Arabidopsis and Nicotiana benthamiana using guide RNA and Cas9. Nat. Biotechnol. 2013, 31:688-691.
    • (2013) Nat. Biotechnol. , vol.31 , pp. 688-691
    • Li, J.F.1
  • 94
    • 84892712729 scopus 로고    scopus 로고
    • Unifying immunology with informatics and multiscale biology
    • Kidd B.A., et al. Unifying immunology with informatics and multiscale biology. Nat. Immunol. 2014, 15:118-127.
    • (2014) Nat. Immunol. , vol.15 , pp. 118-127
    • Kidd, B.A.1
  • 95
    • 60549111634 scopus 로고    scopus 로고
    • WGCNA: an R package for weighted correlation network analysis
    • Langfelder P., Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559.
    • (2008) BMC Bioinformatics , vol.9 , pp. 559
    • Langfelder, P.1    Horvath, S.2
  • 96
    • 84859863444 scopus 로고    scopus 로고
    • Empirically determining the sample size for large-scale gene network inference algorithms
    • Altay G. Empirically determining the sample size for large-scale gene network inference algorithms. IET Syst. Biol. 2012, 6:35-43.
    • (2012) IET Syst. Biol. , vol.6 , pp. 35-43
    • Altay, G.1
  • 97
    • 25644432812 scopus 로고    scopus 로고
    • High throughput screening of co-expressed gene pairs with controlled false discovery rate (FDR) and minimum acceptable strength (MAS)
    • Zhu D.X., et al. High throughput screening of co-expressed gene pairs with controlled false discovery rate (FDR) and minimum acceptable strength (MAS). J. Comput. Biol. 2005, 12:1029-1045.
    • (2005) J. Comput. Biol. , vol.12 , pp. 1029-1045
    • Zhu, D.X.1
  • 98
    • 34249697574 scopus 로고    scopus 로고
    • Approaches for extracting practical information from gene co-expression networks in plant biology
    • Aoki K., et al. Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiol. 2007, 48:381-390.
    • (2007) Plant Cell Physiol. , vol.48 , pp. 381-390
    • Aoki, K.1
  • 99
    • 23944458138 scopus 로고    scopus 로고
    • A general framework for weighted gene co-expression network analysis
    • Zhang B., Horvath S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. 2005, 4:1544-6115.
    • (2005) Stat. Appl. Genet. Mol. , vol.4 , pp. 1544-6115
    • Zhang, B.1    Horvath, S.2
  • 100
    • 27944493925 scopus 로고    scopus 로고
    • Scale-free networks in cell biology
    • Albert R. Scale-free networks in cell biology. J. Cell Sci. 2005, 118:4947-4957.
    • (2005) J. Cell Sci. , vol.118 , pp. 4947-4957
    • Albert, R.1
  • 101
    • 0742305866 scopus 로고    scopus 로고
    • Network biology: understanding the cell's functional organization
    • Barabasi A.L., Oltvai Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 2004, 5:101-113.
    • (2004) Nat. Rev. Genet. , vol.5 , pp. 101-113
    • Barabasi, A.L.1    Oltvai, Z.N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.