The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders
Open accessAbstract
In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .
Overview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“Nor is there a guarantee that any of the hub genes is causal to a phenotype [78].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [78]
In the citing paper
“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 [41]
In the citing paper
“Coexpressed gene sets represent the outcome of transcriptional regulatory mechanisms that include transcription factors, chromatin conformation and latent factors that generate correlations, such as the proportion of different cell types present in the sampled tissue10.”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [10]
In the citing paper
“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 [92]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
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
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
Network pharmacology: the next paradigm in drug discovery
Hopkins, A. L. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.118 · Cited by 4,622
Dynamical Systems in Neuroscience
Izhikevich, E. M. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2526.001.0001 · Cited by 3,859
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242
An anatomically comprehensive atlas of the adult human brain transcriptome
Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., van de Lagemaat, L. N., Smith, K. A., Ebbert, A., Riley, Z. L., et al. (2012) · Nature · doi:10.1038/nature11405 · Cited by 3,083
Intrinsically disordered protein
Dunker, A. K., Lawson, J. D., Brown, C. J., Williams, R. M., Romero, P., Oh, J. S., Oldfield, C. J., Campen, A. M., Ratliff, C. M., Hipps, K. W., et al. (2001) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(00)00138-8 · Cited by 2,272
Belmaker, R. H., & Agam, G. (2008) · New England Journal of Medicine · doi:10.1056/nejmra073096 · Cited by 2,166
Exploratory Social Network Analysis with Pajek
de Nooy, W., Mrvar, A., & Batagelj, V. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108565691 · Cited by 2,089
Spatio-temporal transcriptome of the human brain
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A. M. M., Pletikos, M., Meyer, K. A., Sedmak, G., et al. (2011) · Nature · doi:10.1038/nature10523 · Cited by 2,084
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930
Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874 · cited 4×
“Practically, hubs provide a specific focus for investigations into disease-correlated modules of genes (next section), (Miller et al. 2008; Ray et al. 2008; Torkamani et al. 2010; Voineagu et al. 2011).”
BackgroundInfluentialInteractome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657
Self-citationIntrinsically Disordered Proteins
Uversky, V. N. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-374984-0.01698-3 · Cited by 1,646
BASKERVILLE, S., & BARTEL, D. P. (2005) · RNA · doi:10.1261/rna.7240905 · Cited by 1,508
Architecture of the human regulatory network derived from ENCODE data
Gerstein, M., Kundaje, A. B., Hariharan, M., Landt, S. G., Yan, K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J. S., Alexander, R. P., et al. (2012) · Nature · doi:10.1038/nature11245 · Cited by 1,451
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 1,408
Small-World Networks and Functional Connectivity in Alzheimer's Disease
Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M. J., & Scheltens, P. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhj127 · Cited by 1,295
Exploratory Social Network Analysis with Pajek
de Nooy, W., Mrvar, A., & Batagelj, V. (2005) · doi:10.1017/cbo9780511806452 · Cited by 1,244
Is My Network Module Preserved and Reproducible?
Langfelder, P., Luo, R., Oldham, M. C., & Horvath, S. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001057 · Cited by 1,012
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 911
Major depressive disorder: new clinical, neurobiological, and treatment perspectives
Kupfer, D. J., Frank, E., & Phillips, M. L. (2012) · The Lancet · doi:10.1016/s0140-6736(11)60602-8 · Cited by 894
The mutual information: detecting and evaluating dependencies between variables
Steuer, R., Kurths, J., Daub, C. O., Weise, J., & Selbig, J. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_2.s231 · Cited by 815
Coexpression Analysis of Human Genes Across Many Microarray Data Sets
Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J., & Pavlidis, P. (2004) · Genome Research · doi:10.1101/gr.1910904 · Cited by 770
Functional organization of the transcriptome in human brain
Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., & Geschwind, D. H. (2008) · Nature Neuroscience · doi:10.1038/nn.2207 · Cited by 752 · cited 1×
“This may create cell-type specific modules associated with oligodendrocytes, microglia and several classes of neurons (Hawrylycz et al. 2012; Oldham et al. 2008), as coexpression links within these modules are often driven by the covariance of cell-type markers.”
BackgroundAging effects on DNA methylation modules in human brain and blood tissue
Horvath, S., Zhang, Y., Langfelder, P., Kahn, R. S., Boks, M. P., van Eijk, K. R., van den Berg, L. H., & Ophoff, R. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-10-r97 · Cited by 652
Line graphs, link partitions, and overlapping communities
Evans, T., & Lambiotte, R. (2009) · Physical Review E · doi:10.1103/physreve.80.016105 · Cited by 621
A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS
Vega-Pons, S., & Ruíz-Shulcloper, J. (2011) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001411008683 · Cited by 610
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
Gulsuner, S., Walsh, T., Watts, A. C., Lee, M. K., Thornton, A. M., Casadei, S., Rippey, C., Shahin, H., Braff, D., Cadenhead, K. S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.049 · Cited by 536
Global topological features of cancer proteins in the human interactome
Jonsson, P. F., & Bates, P. A. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl390 · Cited by 498
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
de la Fuente, A. (2010) · Trends in Genetics · doi:10.1016/j.tig.2010.05.001 · Cited by 431
Wolfe, C. J., Kohane, I. S., & Butte, A. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-227 · Cited by 422
Gene Coexpression Networks in Human Brain Identify Epigenetic Modifications in Alcohol Dependence
Ponomarev, I., Wang, S., Zhang, L., Harris, R. A., & Mayfield, R. D. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3136-11.2012 · Cited by 387
A Systems Level Analysis of Transcriptional Changes in Alzheimer's Disease and Normal Aging
Miller, J. A., Oldham, M. C., & Geschwind, D. H. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4098-07.2008 · Cited by 385
DNA methylation signatures in development and aging of the human prefrontal cortex
Numata, S., Ye, T., Hyde, T. M., Guitart-Navarro, X., Tao, R., Wininger, M., Colantuoni, C., Weinberger, D. R., Kleinman, J. E., & Lipska, B. K. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.12.020 · Cited by 364
Quantifying the relationship between co-expression, co-regulation and gene function
Allocco, D. J., Kohane, I. S., & Butte, A. J. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-18 · Cited by 352
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
Network properties of genes harboring inherited disease mutations
Feldman, I., Rzhetsky, A., & Vitkup, D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701722105 · Cited by 294
Genes and pathways underlying regional and cell type changes in Alzheimer’s disease
Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S., & Geschwind, D. H. (2013) · Genome Medicine · doi:10.1186/gm452 · Cited by 275
The Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263
A comparative study of different machine learning methods on microarray gene expression data
Pirooznia, M., Yang, J. Y., Yang, M. Q., & Deng, Y. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-s1-s13 · Cited by 262
Toward the dynamic interactome: it's about time
Przytycka, T. M., Singh, M., & Slonim, D. K. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp057 · Cited by 261
Ripples from neighbouring transcription
Ebisuya, M., Yamamoto, T., Nakajima, M., & Nishida, E. (2008) · Nature Cell Biology · doi:10.1038/ncb1771 · Cited by 253
Molecular aging in human prefrontal cortex is selective and continuous throughout adult life
Erraji-Benchekroun, L., Underwood, M. D., Arango, V., Galfalvy, H., Pavlidis, P., Smyrniotopoulos, P., Mann, J. J., & Sibille, E. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2004.10.034 · Cited by 236
Self-citationHive plots--rational approach to visualizing networks
Krzywinski, M. I., Birol, İ., Jones, S. J. M., & Marra, M. A. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr069 · Cited by 224
Hudson, N. J., Reverter, A., & Dalrymple, B. P. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000382 · Cited by 221
A survey of visualization tools for biological network analysis
Pavlopoulos, G. A., Wegener, A.-L., & Schneider, R. (2008) · BioData Mining · doi:10.1186/1756-0381-1-12 · Cited by 221
“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
Gillis, J., & Pavlidis, P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002444 · Cited by 214
Identification of microRNA-regulated gene networks by expression analysis of target genesPreprint
Gennarino, V. A., D’Angelo, G., Dharmalingam, G., Fernandez, S., Russolillo, G., Sanges, R., M, M., Belcastro, V., Ballabio, A., VERDE, P., et al. (2012) · Genome Research · doi:10.1101/gr.130435.111 · Cited by 201
Identification of transcriptional regulators in the mouse immune system
Jojic, V., Shay, T., Sylvia, K., Zuk, O., Sun, X., Kang, J., Regev, A., & Koller, D. (2013) · Nature Immunology · doi:10.1038/ni.2587 · Cited by 193
Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules
Southworth, L. K., Owen, A. B., & Kim, S. K. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000776 · Cited by 188
VisANT: data-integrating visual framework for biological networks and modules
Hu, Z., Mellor, J. C., Wu, J., Yamada, T., Holloway, D. T., & DeLisi, C. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki431 · Cited by 178
Choi, M., Shi, J., Jung, S. H., Chen, X. X., & Cho, K. (2012) · Science Signaling · doi:10.1126/scisignal.2003363 · Cited by 170
Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression
Amar, D., Safer, H., & Shamir, R. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002955 · Cited by 169
Jothi, R., Balaji, S., Wüster, A., Grochow, J. A., Gsponer, J. A., Przytycka, T. M., Aravind, L., & Babu, M. M. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.52 · Cited by 164
Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease
Dobrin, R., Zhu, J., Molony, C. M., Argman, C., Parrish, M. L., Carlson, S. L., Allan, M. F., Pomp, D., & Schadt, E. E. (2009) · Genome biology · doi:10.1186/gb-2009-10-5-r55 · Cited by 160
Nazarov, P. V., Reinsbach, S. E., Muller, A., Nicot, N., Philippidou, D., Vallar, L., & Kreis, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1471 · Cited by 158
Two gene co-expression modules differentiate psychotics and controls
Chen, C., Cheng, L., Grennan, K., Pibiri, F., Zhang, C., Badner, J. A., Members of the Bipolar Disorder Genome Study (BiGS) Consortium, Gershon, E. S., & Liu, C. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.146 · Cited by 157
Gene-pair expression signatures reveal lineage control
Heinäniemi, M., Nykter, M., Kramer, R., Wienecke-Baldacchino, A. K., Sinkkonen, L., Zhou, J., Kreisberg, R., Kauffman, S. A., Huang, S., & Shmulevich, I. (2013) · Nature Methods · doi:10.1038/nmeth.2445 · Cited by 157
COXPRESdb: a database of coexpressed gene networks in mammals
Obayashi, T., Hayashi, S., Shibaoka, M., Saeki, M., Ohta, H., & Kinoshita, K. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm840 · Cited by 154
Finding disease specific alterations in the co-expression of genes
Kostka, D., & Spang, R. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth909 · Cited by 148
de Jong, S., Boks, M. P., Fuller, T. F., Strengman, E., Janson, E., de Kovel, C. G. F., Ori, A. P. S., Vi, N., Mulder, F., Blom, J. D., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039498 · Cited by 144
Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation
Li, W., Liu, C.-C., Zhang, T., Li, H., Waterman, M. S., & Zhou, X. J. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001106 · Cited by 144
Differential variability analysis of gene expression and its application to human diseases
Ho, J. W. K., Stefani, M., dos Remedios, C. G., & Charleston, M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn142 · Cited by 140
Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies
Barrenäs, F., Chavali, S., Holme, P., Mobini, R., & Benson, M. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008090 · Cited by 137
Alternative α-synuclein transcript usage as a convergent mechanism in Parkinson's disease pathology
Rhinn, H., Qiang, L., Yamashita, T., Rhee, D., Zolin, A., Vanti, W., & Abeliovich, A. (2012) · Nature Communications · doi:10.1038/ncomms2032 · Cited by 137
Variance of Gene Expression Identifies Altered Network Constraints in Neurological Disease
Mar, J. C., Matigian, N., Mackay‐Sim, A., Mellick, G. D., Sue, C. M., Silburn, P. A., McGrath, J. J., Quackenbush, J. F., & Wells, C. A. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002207 · Cited by 136
Torkamani, A., Dean, B., Schork, N. J., & Thomas, E. A. (2010) · Genome Research · doi:10.1101/gr.101956.109 · Cited by 134
Is human blood a good surrogate for brain tissue in transcriptional studies?
Cai, C., Langfelder, P., Fuller, T. F., Oldham, M. C., Luo, R., van den Berg, L. H., Ophoff, R. A., & Horvath, S. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-589 · Cited by 123
Ray, M., Ruan, J., & Zhang, W. (2008) · Genome Biology · doi:10.1186/gb-2008-9-10-r148 · Cited by 119
Iskar, M., Zeller, G., Blattmann, P., Campillos, M., Kuhn, M., Kaminska, K. H., Runz, H., Gavin, A., Pepperkok, R., van Noort, V., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.20 · Cited by 118
Beyond differential expression: the quest for causal mutations and effector molecules
Hudson, N. J., Dalrymple, B. P., & Reverter, A. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-356 · Cited by 113
Molecular signaling network complexity is correlated with cancer patient survivability
Breitkreutz, D. Y., Hlatky, L., Rietman, E. A., & Tuszyński, J. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201416109 · Cited by 104
Integrated analysis of mutations, miRNA and mRNA expression in glioblastoma
Dong, H., Luo, L., Hong, S., Siu, H., Xiao, Y., Jin, L., Chen, R., & Xiong, M. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-163 · Cited by 104
Ivliev, A. E., Hoen, P. A. C. ’., & Sergeeva, M. G. (2010) · Cancer Research · doi:10.1158/0008-5472.can-10-2465 · Cited by 95
Insights into the Organization of Biochemical Regulatory Networks Using Graph Theory Analyses
Ma’ayan, A. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.r800056200 · Cited by 92
Meta-analysis of lineage-specific gene expression signatures in mouse leukocyte populations
Mabbott, N. A., Baillie, J. K., Hume, D. A., & Freeman, T. C. (2010) · Immunobiology · doi:10.1016/j.imbio.2010.05.012 · Cited by 86
Modeling the functional genomics of autism using human neurons
Konopka, G., Wexler, E., Rosen, E., Mukamel, Z., Osborn, G. E., Chen, L., Lu, D., Gao, F., Gao, K., Lowe, J. K., et al. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.60 · Cited by 77
The liver pharmacological and xenobiotic gene response repertoire
Natsoulis, G., Pearson, C. I., Gollub, J., Eynon, B. P., Ferng, J., Nair, R. V., Idury, R., Lee, M. D., Fielden, M. R., Brennan, R., et al. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.9 · Cited by 77
Clarke, C., Doolan, P., Barron, N., Meleady, P., O’Sullivan, F., Gammell, P., Melville, M. W., Leonard, M. W., & CLYNES, M. M. (2011) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2011.07.011 · Cited by 75
Co-expression Profiling of Autism Genes in the Mouse Brain
Menashe, I., Grange, P., Larsen, E. C., Banerjee‐Basu, S., & Mitra, P. P. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003128 · Cited by 68
A systematic comparison of genome-scale clustering algorithms
Jay, J. J., Eblen, J. D., Zhang, Y., Benson, M., Perkins, A., Saxton, A. M., Voy, B. H., Chesler, E. J., & Langston, M. A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s10-s7 · Cited by 64
Combing the hairball with BioFabric: a new approach for visualization of large networks
Longabaugh, W. J. R. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-275 · Cited by 61
Homouz, D., & Kudlicki, A. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054699 · Cited by 55
Marco, A. D., Konikoff, C. E., Karr, T. L., & Kumar, S. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp462 · Cited by 53
Biological substrates underpinning diagnosis of major depression
Sibille, E. L., & French, B. J. (2013) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145713000436 · Cited by 53
Self-citationLocalized network centrality and essentiality in the yeast–protein interaction network
Park, K., & Kim, D. (2009) · PROTEOMICS · doi:10.1002/pmic.200900357 · Cited by 45
Altered Gene Synchrony Suggests a Combined Hormone-Mediated Dysregulated State in Major Depression
Gaiteri, C., Guilloux, J., Lewis, D. A., & Sibille, E. L. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009970 · Cited by 45
Self-citationLee, Y., Li, H., Li, J., Rebman, E., Achour, I., Regan, K., Gamazon, E. R., Chen, J. L., Yang, X. H., Cox, N. J., et al. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001519 · Cited by 42
Analyzing miRNA co-expression networks to explore TF-miRNA regulation
Bandyopadhyay, S., & Bhattacharyya, M. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-163 · Cited by 37
Gaiteri, C., & Sibille, E. L. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00095 · Cited by 34
Self-citationBhattacharyya, M., & Bandyopadhyay, S. (2012) · Molecular BioSystems · doi:10.1039/c2mb25434d · Cited by 32
Niida, A., Imoto, S., Nagasaki, M., Yamaguchi, R., & Miyano, S. (2010) · International Conference on Genome Informatics (GIW) · doi:10.1142/9781848165786_0010 · Cited by 9
Flagged first, then by citations
Research works printed but not linked: 2
- 10.1038/nature10166 · DOI · Paper page
- E.M. Izhikevich 2007 The MIT press Cambridge
Cited by
Sorted by citations
Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., Mohr, D. C., & Schatzberg, A. F. (2016) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.65 · Cited by 2,269
A scalable SCENIC workflow for single-cell gene regulatory network analysis
Van de Sande, B., Flerin, C., Davie, K., De Waegeneer, M., Hulselmans, G., Aibar, S., Seurinck, R., Saelens, W., Cannoodt, R., Rouchon, Q., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0336-2 · Cited by 1,494
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 995
Small-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 887
Co-regulatory networks of human serum proteins link genetics to disease
Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S. R., Aspelund, T., et al. (2018) · Science · doi:10.1126/science.aaq1327 · Cited by 680
Metadata
- Authors
- Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E.
- Affiliations
- Allen Institute for Brain Science (US)
- University of Pittsburgh (US)
- Corresponding author
- Sibille, E.
- Venue
- Genes, Brain and Behavior
- Publisher
- Wiley
- Published
- 2013-12-10
- Volume
- 13
- Issue
- 1
- Pages
- 13-24
- ISSN
- 1601-183X (ISSN-L)
- 1601-1848
- PMC
- PMC3896950
- OpenAlex
- W1823837540
- Cited by
- 289
- References
- 105
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 14 · 2023: 14 · 2022: 25 · 2021: 38 · 2020: 21 · 2019: 24 · 2018: 29 · 2017: 32 · 2016: 34 · 2015: 20 · 2014: 9
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genes Brain & Behavior (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (MH084060, R01MH093723, R01MH077159, K02MH084060)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene co-expression network, neuropsychiatric disorders, hub genes, network modules, network meta-analysis, gene prioritization, network pharmacology, machine learning integration
- MeSH terms
- Animals, Humans, Nervous System Diseases, Transcription, Genetic, Gene Regulatory Networks
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
