Comparing Statistical Methods for Constructing Large Scale Gene Networks
Open accessAbstract
The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
WGCNA and ARACNE perform best at defining the network structure of Escherichia coli.
“A comparison between these tools and others, including WGCNA, showed that WGCNA and ARACNE perform best at defining the network structure of Escherichia coli [130], for which a well-defined regulatory network was used as a gold standard [131].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [130]
Each of the methods detects specific module changes by design, but they can also detect modular changes that they were not specifically designed for and may outperform other tools in the identification of these changes.
“Each of the methods detects specific module changes by design, but they can also detect modular changes that they were not specifically designed for and may outperform other tools in the identification of these changes [130].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [130]
The Results (Hub Gene Detection) and Discussion compare methods for detecting hub genes and constructing gene networks, but do not evaluate changes in modules. A search for module changes and network rewiring found only a Discussion note that WGCNA can detect modules or sub-networks; it does not address whether methods detect changes they were not designed for or outperform other tools at doing so. No page numbers were available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAt currently available sample sizes, networks are most reproducible at module level, then hub gene level, and least at precise gene connectivity rankings or module memberships.
“At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [39]
Weighted networks constructed with topological overlap, such as WGCNA, have similar sensitivity and specificity for detecting true network structure for monotonic relationships as networks constructed with nonlinear association measures such as mutual information, such as ARACNE.
“weighted networks constructed with the topological overlap of correlation (for example, by weighted gene co-expression network analysis (WGCNA)42,44) have similar sensitivity and specificity for detecting true network structure for experiments involving monotonic relationships as do networks constructed with nonlinear association measures such as mutual information (for example, by the Algorithm”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [39]
Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then at the level of precise protein connectivity rankings and module protein membership.
“Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then finally at the level of precise protein connectivity rankings and module protein membership [61, 89–94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [91]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327 · see citing works
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684 · see citing works
A generalized inverse for matrices
Penrose, R. (1955) · Mathematical Proceedings of the Cambridge Philosophical Society · doi:10.1017/s0305004100030401 · Cited by 4,611 · see citing works
A Bayesian Method for the Induction of Probabilistic Networks from Data
Cooper, G. F., & Herskovits, E. H. (1992) · Machine Learning · doi:10.1023/a:1022649401552 · Cited by 4,222 · see citing works
Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s1-s7 · Cited by 2,420 · see citing works
Margolin, A. A., Nemenman, I. M., Basso, K., Wiggins, C. H., Stolovitzky, G. A., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.7916/d8g15zbp · Cited by 2,260 · see citing works
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,235 · see citing works
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,724 · see citing works
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,686 · see citing works
Reverse engineering of regulatory networks in human B cells
Basso, K., Margolin, A. A., Stolovitzky, G. A., Klein, U., Dalla‐Favera, R., & Califano, A. (2005) · Nature Genetics · doi:10.1038/ng1532 · Cited by 1,406 · see citing works
Inferring Cellular Networks Using Probabilistic Graphical Models
Friedman, N. (2004) · Science · doi:10.1126/science.1094068 · Cited by 1,208 · see citing works
The bayes net toolbox for matlab
Murphy, K. (2001) · Cited by 1,014 · see citing works
An empirical Bayes approach to inferring large-scale gene association networks
Schäfer, J., & Strimmer, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti062 · Cited by 786 · see citing works
Revealing modular organization in the yeast transcriptional network
Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y., & Barkai, N. (2002) · Nature Genetics · doi:10.1038/ng941 · Cited by 690 · see citing works
A Probabilistic Functional Network of Yeast Genes
Lee, I., Date, S. V., Adai, A. T., & Marcotte, E. M. (2004) · Science · doi:10.1126/science.1099511 · Cited by 682 · see citing works
Partial Correlation Estimation by Joint Sparse Regression Models
Peng, J., Wang, P., Zhou, N., & Zhu, J. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.0126 · Cited by 657 · see citing works
Horvath, S., Zhang, B., Carlson, M., Lu, K. V., Zhu, S., Felciano, R. M., Laurance, M. F., Zhao, W., Qi, S., Chen, Z., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0608396103 · Cited by 641 · see citing works
Human protein reference database as a discovery resource for proteomics
Peri, S. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh070 · Cited by 606 · see citing works
Human protein reference database--2006 update
Mishra, G. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj141 · Cited by 601 · see citing works
Conservation and evolution of gene coexpression networks in human and chimpanzee brains
Oldham, M. C., Horvath, S., & Geschwind, D. H. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605938103 · Cited by 566 · see citing works
Husmeier, D. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg313 · Cited by 535 · see citing works
Multivariate Dependencies: Models, Analysis and Interpretation.
Murphy, S. A., Cox, D. R., & Wermouth, N. (1998) · Biometrics · doi:10.2307/2534028 · Cited by 494 · see citing works
Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight
Ghazalpour, A., Doss, S., Zhang, B., Wang, S., Plaisier, C., Castellanos, R., Brozell, A., Schadt, E. E., Drake, T. A., Lusis, A. J., et al. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020130 · Cited by 462 · see citing works
Salgado, H. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj156 · Cited by 368 · see citing works
Carter, S. L., Brechbühler, C. M., Griffin, M., & Bond, A. T. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth234 · Cited by 344 · see citing works
Carlson, M. R. J., Zhang, B., Fang, Z., Mischel, P. S., Horvath, S., & Nelson, S. F. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-40 · Cited by 342 · see citing works
Werhli, A. V., Grzegorczyk, M. A., & Husmeier, D. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl391 · Cited by 338 · see citing works
Faith, J. J., Driscoll, M. E., Fusaro, V. A., Cosgrove, E. J., Hayete, B., Juhn, F., Schneider, S. J., & Gardner, T. S. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm815 · Cited by 259 · see citing works
Mason, M. J., Fan, G., Plath, K., Zhou, Q., & Horvath, S. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-327 · Cited by 243 · see citing works
Ruan, J., Dean, A. K., & Zhang, W. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-8 · Cited by 240 · see citing works
Mani, K. M., Lefèbvre, C., Wang, K., Lim, W. K., Basso, K., Dalla‐Favera, R., & Califano, A. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.2 · Cited by 205 · see citing works
Arabidopsis gene co-expression network and its functional modules
Mao, L., Hemert, J. L. V., Dash, S., & Dickerson, J. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-346 · Cited by 200 · see citing works
Learning Causal Bayesian Network Structures From Experimental Data
Ellis, B., & Wong, W. H. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000193 · Cited by 181 · see citing works
B-COURSE: A WEB-BASED TOOL FOR BAYESIAN AND CAUSAL DATA ANALYSIS
Myllymäki, P. J., Silander, T., TIRRI, H., & Uronen, P. (2002) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213002000940 · Cited by 159 · see citing works
An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer
Nibbe, R. K., Koyutürk, M., & Chance, M. R. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000639 · Cited by 153 · see citing works
Model-based cluster analysis of microarray gene-expression data
Pan, W., Lin, J., & Le, C. T. (2002) · Genome biology · doi:10.1186/gb-2002-3-2-research0009 · Cited by 128 · see citing works
VisANT: an integrative framework for networks in systems biology
Hu, Z., Snitkin, E. S., & DeLisi, C. (2008) · Briefings in Bioinformatics · doi:10.1093/bib/bbn020 · Cited by 118 · see citing works
A Network of Genetic Events Sufficient to Convert Normal Human Cells to a Tumorigenic State
Kendall, S., Linardic, C. M., Adam, S. J., & Counter, C. M. (2005) · Cancer Research · doi:10.1158/0008-5472.can-05-1543 · Cited by 108 · see citing works
Chen, X., Chen, M., & Ning, K. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl491 · Cited by 54 · see citing works
Self-citationCorrelation signature of the macroscopic states of the gene regulatory network in cancer
Slavov, N., & Dawson, K. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810803106 · Cited by 49 · see citing works
Multivariate dependencies models, analysis and interpretation
Cox, D. R., & Wermuth, N. (1996) · Cited by 39 · see citing works
Learning Bayesian networks for discrete data
Liang, F., & Zhang, J. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.10.007 · Cited by 25 · see citing works
Exploring Pathways from Gene Co-expression to Network Dynamics
Li, H., Sun, Y., & Zhan, M. (2009) · Methods in molecular biology · doi:10.1007/978-1-59745-243-4_12 · Cited by 24 · see citing works
Alvarez, A. A., & WOOLF, P. J. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-86 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
Parikshak, N. N., Luo, R., Zhang, A., Won, H., Lowe, J. K., Chandran, V., Horvath, S., & Geschwind, D. H. (2013) · Cell · doi:10.1016/j.cell.2013.10.031 · Cited by 1,039 · see citing works
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Unmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047 · see citing works
Aging effects on DNA methylation modules in human brain and blood tissue
Horvath, S., Zhang, Y., Langfelder, P., Kahn, R., Boks, M., van Eijk, K., van den Berg, L. H., & Ophoff, R. (2012) · Genome Biology · doi:10.1186/gb-2012-13-10-r97 · Cited by 635 · see citing works
From big data analysis to personalized medicine for all: challenges and opportunities
Alyass, A., Turcotte, M., & Meyre, D. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0108-y · Cited by 557 · see citing works
Unmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. (2019) · RePEc: Research Papers in Economics · Cited by 580 · see citing works
Comparison of co-expression measures: mutual information, correlation, and model based indices
Song, L., Langfelder, P., & Horvath, S. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-328 · Cited by 460 · see citing works
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works
When is hub gene selection better than standard meta-analysis?
Langfelder, P., Mischel, P. S., & Horvath, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061505 · Cited by 313 · see citing works
Network Organization of the Huntingtin Proteomic Interactome in Mammalian Brain
Shirasaki, D. I., Greiner, E. R., Al-Ramahi, I., Gray, M., Boontheung, P., Geschwind, D. H., Botas, J., Coppola, G., Horvath, S., Loo, J. A., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.05.024 · Cited by 297 · see citing works
The United Kingdom Brain Expression Consortium, Botía, J. A., Vandrovcova, J., Forabosco, P., Guelfi, S., D’Sa, K., Hardy, J., Lewis, C. M., Ryten, M., & Weale, M. E. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0420-6 · Cited by 289 · see citing works
Network structure from rich but noisy data
Newman, M. E. J. (2018) · Nature Physics · doi:10.1038/s41567-018-0076-1 · Cited by 251 · see citing works
Mapping gene regulatory networks from single-cell omics data
Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235 · see citing works
Specht, A. T., & Li, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw729 · Cited by 193 · see citing works
Data Integration Challenges for Machine Learning in Precision Medicine
Martínez-García, M., & Hernández-Lemus, E. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.784455 · Cited by 158 · see citing works
Epigenetic mechanisms and the development of asthma
Yang, I. V., & Schwartz, D. A. (2012) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2012.07.052 · Cited by 151 · see citing works
Epigenetics of idiopathic pulmonary fibrosis
Yang, I. V., & Schwartz, D. A. (2014) · Translational research · doi:10.1016/j.trsl.2014.03.011 · Cited by 142 · see citing works
Enhanced construction of gene regulatory networks using hub gene information
Yu, D., Lim, J., Wang, X., Liang, F., & Xiao, G. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1576-1 · Cited by 139 · see citing works
Self-citationShaik, R., & Ramakrishna, W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077261 · Cited by 128 · see citing works
DINGO: differential network analysis in genomics
Ha, M. J., Baladandayuthapani, V., & Do, K.-A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv406 · Cited by 120 · see citing works
Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau
Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., & Seyfried, N. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-00840-3 · Cited by 118 · see citing works
Genomic Determinants of Protein Abundance Variation in Colorectal Cancer Cells
Roumeliotis, T., Williams, S. P., Gonçalves, E., Alsinet, C., Velasco-Herrera, M. D. C., Aben, N., Ghavidel, F., Michaut, M., Schubert, M., Price, S., et al. (2016) · bioRxiv · doi:10.1016/j.celrep.2017.08.010 · Cited by 103 · see citing works
Gene Coexpression Networks Reveal Key Drivers of Phenotypic Divergence in Lake Whitefish
Filteau, M., Pavey, S. A., St‐Cyr, J., & Bernatchez, L. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst053 · Cited by 111 · see citing works
Review of Biological Network Data and Its Applications
Yu, D., Kim, M., Xiao, G., & Hwang, T. H. (2013) · Genomics & Informatics · doi:10.5808/gi.2013.11.4.200 · Cited by 99 · see citing works
Self-citationMorandin, C., Tin, M. M. Y., Abril, S., Gómez, C., Pontieri, L., Schiøtt, M., Sundström, L., Tsuji, K., Pedersen, J. S., Helanterä, H., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0902-7 · Cited by 97 · see citing works
Tian, Y., Voineagu, I., Paşca, S. P., Won, H., Chandran, V., Horvath, S., Dolmetsch, R. E., & Geschwind, D. H. (2014) · Genome Medicine · doi:10.1186/s13073-014-0075-5 · Cited by 93 · see citing works
Estimating network structure from unreliable measurements
Newman, M. E. J. (2018) · Physical review. E · doi:10.1103/physreve.98.062321 · Cited by 90 · see citing works
Genes associated with ant social behavior show distinct transcriptional and evolutionary patterns
Mikheyev, A. S., & Linksvayer, T. A. (2015) · eLife · doi:10.7554/elife.04775 · Cited by 85 · see citing works
Explainable AI: A review of applications to neuroimaging data
Farahani, F. V., Fiok, K., Lahijanian, B., Karwowski, W., & Douglas, P. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.906290 · Cited by 85 · see citing works
Chu, S. H., Huang, M., Kelly, R., Benedetti, E., Siddiqui, J. K., Zeleznik, O., Pereira, A., Herrington, D., Wheelock, C., Krumsiek, J., et al. (2019) · Metabolites · doi:10.3390/metabo9060117 · Cited by 73 · see citing works
Baldwin, R. L., Wu, S., Li, W., Li, C., Bequette, B. J., & Li, R. W. (2012) · Gene Regulation and Systems Biology · doi:10.4137/grsb.s9687 · Cited by 78 · see citing works
Ma, C., & Wang, X. (2012) · PLANT PHYSIOLOGY · doi:10.1104/pp.112.201962 · Cited by 75 · see citing works
Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network
Chen, J., Wang, X., Hu, B., He, Y., Qian, X., & Wang, W. (2018) · PeerJ · doi:10.7717/peerj.4692 · Cited by 71 · see citing works
Gysi, D. M., Voigt, A., Fragoso, T. D. M., Almaas, E., & Nowick, K. (2018) · NORA - Norwegian Open Research Archives · doi:10.17169/refubium-922 · Cited by 62 · see citing works
Metabolomics of the Tumor Microenvironment in Pediatric Acute Lymphoblastic Leukemia
Tiziani, S., Kang, Y., Harjanto, R., Axelrod, J. L., Piermarocchi, C., Roberts, W. D., & Paternostro, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082859 · Cited by 60 · see citing works
Unraveling gene function in agricultural species using gene co-expression networks
Schaefer, R., Michno, J., & Myers, C. L. (2016) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2016.07.016 · Cited by 60 · see citing works
Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations
Iancu, O. D., Colville, A., Oberbeck, D., Darakjian, P., McWeeney, S. K., & Hitzemann, R. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00174 · Cited by 57 · see citing works
Kelly, R. S., Croteau‐Chonka, D. C., Dahlin, A., Mirzakhani, H., Wu, A. C., Wan, E. S., McGeachie, M. J., Qiu, W., Sordillo, J. E., Al-Garawi, A. A., et al. (2016) · Metabolomics · doi:10.1007/s11306-016-1149-8 · Cited by 57 · see citing works
Gysi, D. M., Voigt, A., Fragoso, T. D. M., Almaas, E., & Nowick, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2351-7 · Cited by 70 · see citing works
ComPlEx: conservation and divergence of co-expression networks in A. thaliana, Populus and O. sativa
Netotea, S., Sundell, D., Street, N. R., & Hvidsten, T. R. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-106 · Cited by 54 · see citing works
An ancient, conserved gene regulatory network led to the rise of oral venom systems
Barua, A., & Mikheyev, A. S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021311118 · Cited by 51 · see citing works
Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data
Huang, J., Vendramin, S., Shi, L., & McGinnis, K. M. (2017) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.00825 · Cited by 51 · see citing works
Gamboa-Tuz, S. D., Pereira‐Santana, A., Zamora‐Briseño, J. A., Castaño, E., Espadas-Gil, F., Ayala-Sumuano, J. T., Keb-Llanes, M. A., Sánchez‐Teyer, L. F., & Rodríguez‐Zapata, L. C. (2018) · Scientific Reports · doi:10.1038/s41598-018-32904-2 · Cited by 51 · see citing works
López-Kleine, L., Leal, L., & López, C. E. (2013) · Briefings in Functional Genomics · doi:10.1093/bfgp/elt003 · Cited by 49 · see citing works
Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2873010 · Cited by 48 · see citing works
Rotival, M., & Petretto, E. G. (2013) · Briefings in Functional Genomics · doi:10.1093/bfgp/elt030 · Cited by 41 · see citing works
Revealing gene regulation and associations through biological networks
Liseron-Monfils, C., & Ware, D. (2015) · Current Plant Biology · doi:10.1016/j.cpb.2015.11.001 · Cited by 43 · see citing works
GeNeCK: a web server for gene network construction and visualization
Zhang, M., Li, Q., Yu, D., Yao, B., Guo, W., Xie, Y., & Xiao, G. (2019) · BMC Bioinformatics · doi:10.1186/s12859-018-2560-0 · Cited by 42 · see citing works
Self-citationYe, X., Yang, Y., Zhao, C., Xiao, S., Sun, Y. H., He, C. L., Xiong, S., Zhao, X., Zhang, B., Lin, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34202-y · Cited by 41 · see citing works
Genetic networks in Parkinson’s and Alzheimer’s disease
Kelly, J., Moyeed, R. A., Carroll, C., Luo, S., & Li, X. (2020) · Aging · doi:10.18632/aging.102943 · Cited by 41 · see citing works
Stability and bifurcation of delay-coupled genetic regulatory networks with hub structure
Yue, D., Guan, Z., Li, J., Liu, F., Xiao, J., & Ling, G. (2019) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2018.11.030 · Cited by 39 · see citing works
Differential analysis of biological networks
Ruan, D., YOUNG, G. A., & Montana, G. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0735-5 · Cited by 38 · see citing works
Kunowska, N., Rotival, M., Yu, L., Choudhary, J. S., & Dillon, N. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1350 · Cited by 38 · see citing works
Emamjomeh, A., Robat, E. S., Zahiri, J., Solouki, M., & Khosravi, P. (2017) · Plant Biotechnology Reports · doi:10.1007/s11816-017-0433-z · Cited by 38 · see citing works
Detection of statistically significant network changes in complex biological networks
Mall, R., Cerulo, L., Bensmail, H., Iavarone, A., & Ceccarelli, M. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0412-6 · Cited by 38 · see citing works
Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction
Zhong, R., Allen, J. D., Xiao, G., & Xie, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106319 · Cited by 37 · see citing works
Self-citationConstruction and Analysis of Gene Co-Expression Networks in Escherichia coli
Liu, W., Li, L., Long, X., You, W., Zhong, Y., Wang, M., Tao, H., Lin, S., & He, H. (2018) · Cells · doi:10.3390/cells7030019 · Cited by 37 · see citing works
Seasonal immunoregulation in a naturally-occurring vertebrate
Brown, M. J., Hablützel, P. I., Friberg, I. M., Thomason, A. G., Stewart, A., Pachebat, J. A., & Jackson, J. A. (2016) · BMC Genomics · doi:10.1186/s12864-016-2701-7 · Cited by 36 · see citing works
Palau, N., Julià, A., Ferrándiz, C., Puig, L., Fonseca, E., Fernández, E., Lasanta, M. L., Tortosa, R., & Marsal, S. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-825 · Cited by 34 · see citing works
Gene coexpression measures in large heterogeneous samples using count statistics
Wang, Y. X. R., Waterman, M. S., & Huang, H. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1417128111 · Cited by 33 · see citing works
Wang, J., Wu, G., Chen, L., & Zhang, W. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-112 · Cited by 33 · see citing works
Smita, S., Katiyar, A., Lenka, S. K., Dalal, M., Kumar, A., Mahtha, S. K., Yadav, G., Chinnusamy, V., Pandey, D. M., & Bansal, K. C. (2019) · Functional & Integrative Genomics · doi:10.1007/s10142-019-00697-w · Cited by 32 · see citing works
Bayesian nonlinear model selection for gene regulatory networks
Ni, Y., Stingo, F. C., & Baladandayuthapani, V. (2015) · Biometrics · doi:10.1111/biom.12309 · Cited by 32 · see citing works
Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis
Flores, M. A., Hsiao, T.-H., Chiu, Y., Chuang, E. Y.-Y., Huang, Y., & Chen, Y. (2013) · Advances in Bioinformatics · doi:10.1155/2013/360678 · Cited by 31 · see citing works
Functional Genomic Analyses Identify Pathways Dysregulated in Animal Model of Autism
Huang, J., Tian, Y., Wang, H., Shen, H., Wang, H., Long, S., Liao, M., Liu, Z., Wang, Z., Li, D., et al. (2016) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.12582 · Cited by 31 · see citing works
Chen, J., Qian, X., He, Y., Han, X., & Pan, Y. (2019) · Journal of Cellular Biochemistry · doi:10.1002/jcb.28948 · Cited by 29 · see citing works
Stability Indicators in Network Reconstruction
Filosi, M., Visintainer, R., Riccadonna, S., Jurman, G., & Furlanello, C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089815 · Cited by 26 · see citing works
RapidMic: Rapid Computation of the Maximal Information Coefficient
Tang, D., Wang, M., Zheng, W., & Wang, H. (2014) · Evolutionary Bioinformatics · doi:10.4137/ebo.s13121 · Cited by 27 · see citing works
Peña‐Castillo, L., Mercer, R. G., Gurinovich, A., Callister, S., Wright, A., Westbye, A. B., Beatty, J. T., & Lang, A. S. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-730 · Cited by 26 · see citing works
Brain transcriptome atlases: a computational perspective
Mahfouz, A. E., Huisman, S. M. H., Lelieveldt, B. P. F., & Reinders, M. J. T. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1338-2 · Cited by 26 · see citing works
Quantitative Transcriptome Analysis Using RNA-seq
Külahoglu, C., & Bräutigam, A. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0700-7_5 · Cited by 24 · see citing works
Shannon entropy approach reveals relevant genes in Alzheimer’s disease
Monaco, A., Amoroso, N., Bellantuono, L., Lella, E., Lombardi, A., Monda, A., Tateo, A., Bellotti, R., & Tangaro, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226190 · Cited by 22 · see citing works
Construction, comparison and evolution of networks in life sciences and other disciplines
Gysi, D. M., & Nowick, K. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0610 · Cited by 25 · see citing works
Das, S. K., & Sharma, N. (2014) · World Journal of Diabetes · doi:10.4239/wjd.v5.i2.97 · Cited by 22 · see citing works
Probing the network structure of health deficits in human aging
Farrell, S. G., Mitnitski, A., Theou, O., Rockwood, K. J., & Rutenberg, A. D. (2018) · Physical review. E · doi:10.1103/physreve.98.032302 · Cited by 21 · see citing works
Broadbent, J. A., Sampson, D. L., Sabapathy, S., Haseler, L. J., Wagner, K., Bulmer, A. C., Peake, J. M., & Neubauer, O. (2017) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00114.2016 · Cited by 21 · see citing works
Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules
Iancu, O. D., Oberbeck, D. L., Darakjian, P., Kawane, S., Erk, J., McWeeney, S. K., & HITZEMANN, R. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058951 · Cited by 20 · see citing works
Kober, K. M., Schumacher, M. A., Conley, Y. P., Topp, K. S., Mazor, M. B., Hammer, M. J., Paul, S. M., Levine, J. D., & Miaskowski, C. A. (2019) · Molecular Pain · doi:10.1177/1744806919878088 · Cited by 19 · see citing works
Breen, M. S., Stein, D. J., & Baldwin, D. S. (2016) · Human Psychopharmacology Clinical and Experimental · doi:10.1002/hup.2546 · Cited by 18 · see citing works
Applying systems biology in drug discovery and development
Galizzi, J., Lockhart, B., & Bril, A. (2013) · Drug Metabolism and Drug Interactions · doi:10.1515/dmdi-2013-0002 · Cited by 12 · see citing works
Gao, C., Ju, Z., Li, S., Zuo, J., Fu, D., Tian, H., Luo, Y., & Zhu, B. (2013) · Journal of Integrative Plant Biology · doi:10.1111/jipb.12079 · Cited by 18 · see citing works
Network entropy: A system-theoretic perspective
de Badyn, M. H., Chapman, A., & Mesbahi, M. (2015) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2015.7403083 · Cited by 18 · see citing works
Guo, W., Calixto, C. P. G., Τzioutziou, N. A., Lin, P., Waugh, R., Brown, J. W. S., & Zhang, R. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0440-2 · Cited by 18 · see citing works
O’Meara, M. J., Ballouz, S., Shoichet, B. K., & Gillis, J. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160098 · Cited by 18 · see citing works
Liu, H., Li, P., Zhu, M., Wang, X., Lu, J., & Yu, T. (2016) · PLOS ONE · doi:10.1371/journal.pone.0158247 · Cited by 17 · see citing works
Weiser, M., Mukherjee, S., & Furey, T. S. (2014) · Genetics · doi:10.1534/genetics.114.167791 · Cited by 16 · see citing works
Willis, C. R. G., Gallagher, I. J., Wilkinson, D. J., Brook, M. S., Bass, J. J., Phillips, B. E., Smith, K., Etheridge, T., Stokes, T., McGlory, C., et al. (2021) · The FASEB Journal · doi:10.1096/fj.202100276rr · Cited by 17 · see citing works
Reconstruction and Analysis of Cancerspecific Gene Regulatory Networks from Gene Expression Profiles
Raza, K., & Jaiswal, R. (2013) · International Journal on Bioinformatics & Biosciences · doi:10.5121/ijbb.2013.3203 · Cited by 16 · see citing works
Wilzén, A., Krona, C. C., Sveinbjørnsson, B., Kristiansson, E., Dalevi, D., Øra, I., Preter, K. D., Stallings, R. L., Maris, J. M., Versteeg, R., et al. (2013) · Molecular Cancer · doi:10.1186/1476-4598-12-70 · Cited by 16 · see citing works
DTW-MIC Coexpression Networks from Time-Course Data
Riccadonna, S., Jurman, G., Visintainer, R., Filosi, M., & Furlanello, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152648 · Cited by 17 · see citing works
Mo, X., Wu, L., Zhu, X.-W., Xia, W., Wang, L., He, P., Bing, P., Lu, X., Zhang, Y., Deng, F., et al. (2017) · Epigenomics · doi:10.2217/epi-2016-0178 · Cited by 15 · see citing works
Network analysis of human muscle adaptation to aging and contraction
Willis, C. R. G., Ames, R. M., Deane, C. S., Phillips, B. E., Boereboom, C., Abdulla, H., Bukhari, S. S. I., Lund, J. N., Williams, J. P., Wilkinson, D. J., et al. (2020) · Aging · doi:10.18632/aging.102653 · Cited by 15 · see citing works
Coexpression and Cosplicing Network Approaches for the Study of Mammalian Brain Transcriptomes
Iancu, O. D., Colville, A. M., Darakjian, P., & HITZEMANN, R. J. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-801105-8.00004-7 · Cited by 12 · see citing works
Martin, J., Berton, A., Giniès, C., Bott, R., Scheercousse, P., Saddi, A., Gripois, D., Landrier, J., Dalemans, D., Alessi, M., et al. (2015) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00032.2015 · Cited by 14 · see citing works
Lin, C., Huang, L.-Y., Huang, C., Wang, Y.-C., Lai, P.-H., Wang, H., Chang, W., Chiang, T., & Huang, H.-J. (2017) · Plant and Cell Physiology · doi:10.1093/pcp/pcx002 · Cited by 14 · see citing works
Dehghanian, F., Hojati, Z., Esmaeili, F., & Masoudi‐Nejad, A. (2018) · Genomics · doi:10.1016/j.ygeno.2018.05.007 · Cited by 14 · see citing works
Mo, X., Wu, L., Lu, X., Zhu, X.-W., Xia, W., Wang, L., He, P., Bing, P., Zhang, Y., Deng, F., et al. (2018) · Functional & Integrative Genomics · doi:10.1007/s10142-018-0638-4 · Cited by 14 · see citing works
Wang, P., Zheng, H., Zhang, J., Wang, Y., Liu, P., Xuan, X., Li, Q., & Du, Y. (2020) · Bioscience Reports · doi:10.1042/bsr20202044 · Cited by 14 · see citing works
Ravichandran, S., & Chandra, N. R. (2019) · International Journal of Advances in Engineering Sciences and Applied Mathematics · doi:10.1007/s12572-018-0242-9 · Cited by 13 · see citing works
He, M., Li, X., Mang, M., Li, Z., Ludewig, U., & Schulze, W. X. (2021) · The Plant Journal · doi:10.1111/tpj.15630 · Cited by 13 · see citing works
- Authors
- Allen, J. D., Xie, Y., Chen, M., Girard, L., & Xiao, G.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- Corresponding author
- Xiao, Guanghua
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-01-17
- Volume
- 7
- Issue
- 1
- Pages
- e29348
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3260142
- OpenAlex
- W2061971803
- Cited by
- 186
- References
- 46
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 1 · 2022: 4 · 2021: 13 · 2020: 11 · 2019: 17 · 2018: 15 · 2017: 22 · 2016: 20 · 2015: 25 · 2014: 15 · 2013: 24 · 2012: 9
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (0907562)
- National Institutes of Health (RR024982)
- National Institute on Drug Abuse (R33DA027592, R21DA027592)
- National Cancer Institute ((P50-CA70907, R01CA152301, P50CA070907)
- National Center for Research Resources (UL1RR024982)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- gene regulatory networks, network inference, weighted gene co-expression network analysis, ARACNE, hub genes, sensitivity and specificity, Escherichia coli, method comparison
- MeSH terms
- Algorithms, Escherichia coli, Reproducibility of Results, Computational Biology, Protein Interaction Mapping, Escherichia coli Proteins, Gene Regulatory Networks, Protein Interaction Maps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
