Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks
Open accessAbstract
Background: Biological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA) heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain. Results: We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO)? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories. Conclusion: We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing whether pairs of functions operate together.
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Gene products involved in an unknown cell type or biological process can be annotated based on their proximity to marker genes of known function.
“Thus, gene products that are involved in an unknown cell type or biological process can be annotated on the basis of their proximity to marker genes of known function (‘guilt by association’)26,33,34.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [33]
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Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 12,617 · see citing works
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197 · see citing works
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632 · see citing works
Functional Discovery via a Compendium of Expression Profiles
Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R. B., Armour, C. D., Bennett, H. A., Coffey, E. M., Dai, H. A., He, Y. D., et al. (2000) · Cell · doi:10.1016/s0092-8674(00)00015-5 · Cited by 2,534 · see citing works
Systematic determination of genetic network architecture
Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., & Church, G. M. (1999) · Nature Genetics · doi:10.1038/10343 · Cited by 2,509 · see citing works
Exploring the new world of the genome with DNA microarrays
Brown, P. O., & Botstein, D. (1999) · Nature Genetics · doi:10.1038/4462 · Cited by 2,430 · 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
Computational analysis of microarray data
Quackenbush, J. (2001) · Nature Reviews Genetics · doi:10.1038/35076576 · Cited by 1,481 · see citing works
Jeffery, C. J. (1999) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(98)01335-8 · Cited by 1,084 · see citing works
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 · 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 module map showing conditional activity of expression modules in cancer
Segal, E., Friedman, N., Koller, D., & Regev, A. (2004) · Nature Genetics · doi:10.1038/ng1434 · Cited by 685 · see citing works
Drăghici, S., Khatri, P., Martins, R. P., Ostermeier, G. C., & Krawetz, S. A. (2003) · Genomics · doi:10.1016/s0888-7543(02)00021-6 · Cited by 578 · see citing works
Standardizing global gene expression analysis between laboratories and across platforms
Weis, B. K. (2005) · Nature Methods · doi:10.1038/nmeth754 · Cited by 465 · see citing works
McCarroll, S. A., Murphy, C. T., Zou, S., Pletcher, S. D., Chin, C.-S., Jan, Y. N., Kenyon, C., Bargmann, C. I., & Li, H. (2004) · Nature Genetics · doi:10.1038/ng1291 · Cited by 454 · see citing works
An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis.
Sweet-Cordero, A., Mukherjee, S., Subramanian, A., You, H., Roix, J. J., Ladd-Acosta, C., Mesirov, J., Golub, T. R., & Jacks, T. (2005) · Nature Genetics · doi:10.1038/ng1490 · Cited by 414 · see citing works
A Mechanism of Cyclin D1 Action Encoded in the Patterns of Gene Expression in Human Cancer
Lamb, J., Ramaswamy, S., Ford, H. L., Contreras, B., Martinez, R. V. P., Kittrell, F. S., Zahnow, C. A., Patterson, N. J., Golub, T. R., & Ewen, M. E. (2003) · Cell · doi:10.1016/s0092-8674(03)00570-1 · Cited by 410 · see citing works
Wu, L. F., Hughes, T. R., Davierwala, A. P., Robinson, M. D., Stoughton, R. B., & Altschuler, S. J. (2002) · Nature Genetics · doi:10.1038/ng906 · Cited by 349 · see citing works
The functional landscape of mouse gene expression
Zhang, W., Morris, Q., Chang, R., Shai, O., Bakowski, M. A., Mitsakakis, N., Mohammad, N., Robinson, M. D., Zirngibl, R. A., Somogyi, E., et al. (2004) · Journal of Biology · doi:10.1186/jbiol16 · Cited by 312 · see citing works
A probabilistic view of gene function
Fraser, A. G., & Marcotte, E. M. (2004) · Nature Genetics · doi:10.1038/ng1370 · Cited by 129 · see citing works
Data analysis and integration: of steps and arrows
Bittner, M. L., Meltzer, P. A., & Trent, J. M. (1999) · Nature Genetics · doi:10.1038/10265 · Cited by 93 · see citing works
How well do we understand the clusters found in microarray data?
Clare, A. J., & King, R. D. (2002) · PubMed · Cited by 31 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Gut microbiome structure and metabolic activity in inflammatory bowel disease
Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall, A. B., Mallick, H., McIver, L. J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0306-4 · Cited by 1,995 · see citing works
Buffa, F. M., Harris, A. L., West, C., & Miller, C. J. (2010) · British Journal of Cancer · doi:10.1038/sj.bjc.6605450 · Cited by 558 · see citing works
Co‐expression tools for plant biology: opportunities for hypothesis generation and caveats
Usadel, B., Obayashi, T., Mutwil, M., Giorgi, F. M., Bassel, G. W., Tanimoto, M., CHOW, A., Steinhauser, D., Persson, S., & Provart, N. J. (2009) · Plant Cell & Environment · doi:10.1111/j.1365-3040.2009.02040.x · Cited by 476 · 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
Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics
Zhang, B., VerBerkmoes, N. C., Langston, M. A., Uberbacher, E. C., Hettich, R. L., & Samatova, N. F. (2006) · Journal of Proteome Research · doi:10.1021/pr0600273 · Cited by 394 · see citing works
Inferring cellular networks – a review
Markowetz, F. M., & Spang, R. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-s6-s5 · Cited by 391 · see citing works
Systems medicine: the future of medical genomics and healthcare
Auffray, C., Zhu, C. H., & Hood, L. (2009) · Genome Medicine · doi:10.1186/gm2 · Cited by 386 · see citing works
Gene expression analysis identifies global gene dosage sensitivity in cancer
Fehrmann, R. S. N., Karjalainen, J. M., Krajewska, M., Westra, H.-J., Maloney, D., Simeonov, A., Pers, T. H., Hirschhorn, J. N., Jansen, R. C., Schultes, E. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3173 · Cited by 347 · see citing works
Reverter, A., & Chan, E. K. F. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn482 · Cited by 361 · see citing works
Multiscale Embedded Gene Co-expression Network Analysis
Song, W., & Zhang, B. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004574 · Cited by 357 · see citing works
Aubin‐Horth, N., & Renn, S. C. P. (2009) · Molecular Ecology · doi:10.1111/j.1365-294x.2009.04313.x · Cited by 354 · 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
Decoding genes with coexpression networks and metabolomics – ‘majority report by precogs’
Saito, K., Hirai, M. Y., & Yonekura-Sakakibara, K. (2008) · Trends in Plant Science · doi:10.1016/j.tplants.2007.10.006 · Cited by 327 · see citing works
Learning from Co-expression Networks: Possibilities and Challenges
Serin, E. A. R., Nijveen, H., Hilhorst, H., & Ligterink, W. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.00444 · Cited by 272 · see citing works
Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12106 · Cited by 289 · see citing works
Metabolomic approaches toward understanding nitrogen metabolism in plants
Kusano, M., Fukushima, A., Redestig, H., & Saito, K. (2011) · Journal of Experimental Botany · doi:10.1093/jxb/erq417 · Cited by 260 · see citing works
Reiss, D. J., Baliga, N. S., & Bonneau, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-280 · Cited by 253 · see citing works
Vandepoele, K., Quimbaya, M., Casneuf, T., Veylder, L. D., & de Peer, Y. V. (2009) · PLANT PHYSIOLOGY · doi:10.1104/pp.109.136028 · Cited by 225 · see citing works
“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 · see citing works
Predicting Essential Proteins Based on Weighted Degree Centrality
Tang, X., Wang, J., Zhong, J., & Pan, Y. F. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.2295318 · Cited by 197 · see citing works
The Impact of Multifunctional Genes on "Guilt by Association" Analysis
Gillis, J., & Pavlidis, P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017258 · Cited by 175 · see citing works
Review on statistical methods for gene network reconstruction using expression data
Wang, Y. X. R., & Huang, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.040 · Cited by 182 · see citing works
Reverter, A., Hudson, N. J., Nagaraj, S. H., Miguel Pérez-enciso, A., & Dalrymple, B. P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq051 · Cited by 173 · see citing works
De novo assembly of a Chinese soybean genome
Shen, Y.-T., Liu, J., Geng, H., Zhang, J.-X., Liu, Y.-C., Zhang, H., Xing, S., Du, J.-C., Ma, S.-S., & Tian, Z. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9360-0 · Cited by 148 · see citing works
Swindell, W. R. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-585 · Cited by 171 · see citing works
Functional Modules in the Arabidopsis Core Cell Cycle Binary Protein–Protein Interaction Network
Boruc, J., den Daele, H. V., Hollunder, J., Rombauts, S., Mylle, E., Hilson, P., Inzé, D., Veylder, L. D., & Russinova, E. (2010) · The Plant Cell · doi:10.1105/tpc.109.073635 · Cited by 169 · see citing works
Annotating Genes of Known and Unknown Function by Large-Scale Coexpression Analysis
Horan, K., Jang, C. J. H., Bailey-Serres, J. N., Mittler, R., Shelton, C. R., Harper, J. F., Zhu, J., Cushman, J. C., Gollery, M., & Girke, T. (2008) · PLANT PHYSIOLOGY · doi:10.1104/pp.108.117366 · Cited by 166 · see citing works
Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize
Schaefer, R., Michno, J., Jeffers, J. R., Hoekenga, O. A., Dilkes, B. P., Baxter, I. R., & Myers, C. L. (2018) · The Plant Cell · doi:10.1105/tpc.18.00299 · Cited by 158 · see citing works
Tao, Y., Sam, L. T., Li, J., Friedman, C., & Lussier, Y. A. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm195 · Cited by 159 · see citing works
Computational approaches to disease‐gene prediction: rationale, classification and successes
Piro, R. M., & Cunto, F. D. (2012) · FEBS Journal · doi:10.1111/j.1742-4658.2012.08471.x · Cited by 156 · see citing works
Adler, P., Kolde, R., Kull, M., Tkachenko, A., Peterson, H., Reimand, J., & Vilo, J. (2009) · Genome biology · doi:10.1186/gb-2009-10-12-r139 · Cited by 151 · see citing works
Ghazalpour, A., Rau, C. D., Farber, C. R., Bennett, B. J., Orozco, L. D., van Nas, A., Pan, C., Allayee, H., Beaven, S. W., Civelek, M., et al. (2012) · Mammalian Genome · doi:10.1007/s00335-012-9411-5 · Cited by 146 · see citing works
Ficklin, S. P., & Feltus, F. A. (2011) · PLANT PHYSIOLOGY · doi:10.1104/pp.111.173047 · Cited by 141 · see citing works
Emmert‐Streib, F., Glazko, G. V., Altay, G., & Simoes, R. D. M. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00008 · Cited by 125 · see citing works
From genes to functional classes in the study of biological systems
Al‐Shahrour, F., Arbiza, L., Dopazo, H., Huerta‐Cepas, J., Mínguez, P., Montaner, D. A., & Dopazo, J. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-114 · Cited by 122 · see citing works
Anderson, K. M., Collins, M. A., Chin, R., Ge, T., Rosenberg, M. D., & Holmes, A. J. (2020) · Nature Communications · doi:10.1038/s41467-020-16710-x · Cited by 119 · see citing works
Computational inference of gene regulatory networks: Approaches, limitations and opportunities
Banf, M., & Rhee, S. Y. (2016) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2016.09.003 · Cited by 120 · see citing works
Heyndrickx, K. S., & Vandepoele, K. (2012) · PLANT PHYSIOLOGY · doi:10.1104/pp.112.196725 · Cited by 115 · see citing works
Farber, C. R., Bennett, B. J., Orozco, L. D., Zou, W., Lira, A. K., Kostem, E., Kang, H. M., Furlotte, N. A., Berberyan, A., Ghazalpour, A., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002038 · Cited by 113 · see citing works
Extracting Gene Networks for Low-Dose Radiation Using Graph Theoretical Algorithms
Voy, B. H., Scharff, J. A., Perkins, A., Saxton, A. M., Borate, B. R., Chesler, E. J., Branstetter, L. R., & Langston, M. A. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020089 · Cited by 111 · see citing works
Giorgi, F. M., Del Fabbro, C., & Licausi, F. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt053 · Cited by 103 · see citing works
Vella, D., Zoppis, I. F., Mauri, G., Mauri, P., & Silvestre, D. D. (2017) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1186/s13637-017-0059-z · Cited by 103 · see citing works
Ficklin, S. P., Luo, F., & Feltus, F. A. (2010) · PLANT PHYSIOLOGY · doi:10.1104/pp.110.159459 · Cited by 102 · see citing works
New perspectives on glutamine synthetase in grasses
Swarbreck, S. M., Platel, M. D., Hindle, M., Saqi, M. A. S., & Habash, D. Z. (2010) · Journal of Experimental Botany · doi:10.1093/jxb/erq356 · Cited by 101 · see citing works
Salavaty, A., Ramialison, M., & Currie, P. D. (2020) · Patterns · doi:10.1016/j.patter.2020.100052 · Cited by 99 · see citing works
Co-expression networks for plant biology: why and how
Rao, X., & Dixon, R. A. (2019) · Acta Biochimica et Biophysica Sinica · doi:10.1093/abbs/gmz080 · Cited by 96 · see citing works
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76 · see citing works
HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction
Yang, K., Wang, R., Liu, G., Shu, Z., Wang, N., Zhang, R., Yu, J. Q., Chen, J., Li, X., & Zhou, X. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2870728 · Cited by 94 · see citing works
The Complex Regulation of Senescence
Fischer, A. M. (2012) · Critical Reviews in Plant Sciences · doi:10.1080/07352689.2011.616065 · Cited by 93 · see citing works
Functional Interpretation of Microarray Experiments
Dopazo, J. (2006) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2006.10.398 · Cited by 90 · see citing works
Threshold selection in gene co-expression networks using spectral graph theory techniques
Perkins, A., & Langston, M. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s11-s4 · Cited by 87 · see citing works
Peng, F. Y., & Weselake, R. J. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-286 · Cited by 86 · see citing works
Liu, W.-T., Wang, Y., Zhang, J., Ye, F., Huang, X., Li, B., & He, Q. (2018) · Cancer Letters · doi:10.1016/j.canlet.2018.03.043 · Cited by 85 · see citing works
Gene Coexpression Networks for the Analysis of DNA Microarray Data
Weirauch, M. T. (2011) · Wiley eBooks · doi:10.1002/9783527638079.ch11 · Cited by 85 · see citing works
Chen, Y., Guo, Y., Guan, P., Wang, Y., Wang, X., Wang, Z., Qin, Z., Ma, S., Xin, M., Hu, Z., et al. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.12.019 · Cited by 84 · see citing works
Probability-based collaborative filtering model for predicting gene–disease associations
Zeng, X., Ding, N., Rodríguez‐Patón, A., & Zou, Q. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0313-y · Cited by 82 · see citing works
Global similarity and local divergence in human and mouse gene co-expression networks
Tsaparas, P., Mariño‐Ramírez, L., Bodenreider, O., Koonin, E. V., & Jordan, I. K. (2006) · BMC Evolutionary Biology · doi:10.1186/1471-2148-6-70 · Cited by 79 · see citing works
Integrating gene regulatory pathways into differential network analysis of gene expression data
Grimes, T., Potter, S. S., & Datta, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-41918-3 · Cited by 77 · see citing works
Reverter, A., Ingham, A. B., Lehnert, S. A., Tan, S. H., Wang, Y., Ratnakumar, A., & Dalrymple, B. P. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl392 · Cited by 75 · see citing works
Shahan, R., Zawora, C., Wight, H., Sittmann, J., Wang, W., Mount, S. M., & Liu, Z. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.00086 · Cited by 77 · see citing works
Labor-Associated Gene Expression in the Human Uterine Fundus, Lower Segment, and Cervix
Bukowski, R., Hankins, G. D. V., Saade, G. R., Anderson, G. D., & Thornton, S. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030169 · Cited by 77 · see citing works
The role of indirect connections in gene networks in predicting function
Gillis, J., & Pavlidis, P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr288 · Cited by 73 · see citing works
(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2893170 · Cited by 75 · see citing works
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 74 · see citing works
Wu, S., Liu, H., Huang, P.-J., Chang, I. Y., Lee, C., Yang, C.-Y., Tsai, W., & Tan, B. C. (2017) · GigaScience · doi:10.1093/gigascience/gix118 · Cited by 72 · see citing works
A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease
Timmons, J. A., Atherton, P. J., Larsson, O. S., Sood, S., Blokhin, I. O., Brogan, R. J., Volmar, C., Josse, A. R., Slentz, C. A., Wahlestedt, C., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky570 · Cited by 68 · see citing works
Identification of the 3-lncRNA Signature as a Prognostic Biomarker for Colorectal Cancer
Liu, S., Cao, Q., An, G., Yan, B., & Lei, L. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21249359 · Cited by 68 · see citing works
A novel essential protein identification method based on PPI networks and gene expression data
Zhong, J., Tang, C., Peng, W., Xie, M., Sun, Y., Tang, Q., Xiao, Q., & Yang, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04175-8 · Cited by 67 · see citing works
Wong, D. C. J., & Matus, J. T. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.00505 · Cited by 67 · see citing works
Itzel, T., Scholz, P., Maass, T., Krupp, M., Marquardt, J. U., Strand, S., Becker, D., Staib, F., Binder, H., Roessler, S. K., et al. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu586 · Cited by 66 · see citing works
Kwenda, S., Birch, P. R. J., & Moleleki, L. (2016) · BMC Genomics · doi:10.1186/s12864-016-2967-9 · Cited by 65 · see citing works
CLEAN: CLustering Enrichment ANalysis
Freudenberg, J., Joshi, V. K., Hu, Z., & Medvedovic, M. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-234 · Cited by 64 · see citing works
Swindell, W. R., Johnston, A. W. B., Xing, X., Voorhees, J. J., Elder, J. T., & Gudjonsson, J. E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079253 · Cited by 63 · see citing works
Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density
Mesner, L. D., Ray, B. J., Hsu, Y., Manichaikul, A. W., Lum, E., Bryda, E. C., Rich, S. S., Rosen, C. J., Criqui, M. H., Allison, M., et al. (2014) · Journal of Clinical Investigation · doi:10.1172/jci73072 · Cited by 59 · 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
Orduña, L., Li, M., Navarro‐Payá, D., Zhang, C., Santiago, A., Romero, P., Ramšak, Ž., Magon, G., Höll, J., Merz, P., et al. (2022) · The Plant Journal · doi:10.1111/tpj.15686 · Cited by 60 · see citing works
Mahfouz, A. E., Ziats, M. N., Rennert, O. M., Lelieveldt, B. P. F., & Reinders, M. J. T. (2015) · Journal of Molecular Neuroscience · doi:10.1007/s12031-015-0641-3 · Cited by 58 · see citing works
Gibson, S. M., Ficklin, S. P., Isaacson, S., Luo, F., Feltus, F. A., & Smith, M. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055871 · Cited by 58 · see citing works
A knowledge-based approach for predicting gene–disease associations
Zhou, H., & Skolnick, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw358 · Cited by 55 · see citing works
Functional screen of inflammatory bowel disease genes reveals key epithelial functions
Ntunzwenimana, J. C., Boucher, G., Paquette, J., Gosselin, H., Alikashani, A., Morin, N., Beauchamp, C., Thauvette, L., Rivard, M.-È., Dupuis, F., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00996-7 · Cited by 53 · see citing works
Gene Network Reconstruction by Integration of Prior Biological Knowledge
Li, Y., & Jackson, S. A. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.115.018127 · Cited by 54 · see citing works
PP2A-3 interacts with ACR4 and regulates formative cell division in the Arabidopsis root
Yue, K., Sandal, P., Williams, E. L., Murphy, E., Stes, E., Nikonorova, N., Ramakrishna, P., Czyzewicz, N., Montero‐Morales, L., Kumpf, R. P., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1525122113 · Cited by 54 · see citing works
EDISA: extracting biclusters from multiple time-series of gene expression profiles
Supper, J., Strauch, M., Wanke, D., Harter, K., & Zell, A. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-334 · Cited by 52 · see citing works
Narayanasamy, S., Muller, E. E. L., Sheik, A., & Wilmes, P. (2015) · Microbial Biotechnology · doi:10.1111/1751-7915.12255 · Cited by 52 · see citing works
Masalia, R. R., Bewick, A. J., & Burke, J. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182289 · 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
The unfolded protein response links tumor aneuploidy to local immune dysregulation
Xian, S., Dosset, M., Almanza, G., Searles, S. C., Sahani, P., Waller, T. C., Jepsen, K., Carter, H., & Zanetti, M. (2020) · bioRxiv · doi:10.15252/embr.202152509 · Cited by 43 · see citing works
HENA, heterogeneous network-based data set for Alzheimer’s disease
Sügis, E., Dauvillier, J., Leontjeva, A., Adler, P., Hindie, V., Moncion, T., Collura, V., Daudin, R., Loe-Mie, Y., Hérault, Y., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0152-0 · Cited by 50 · see citing works
Druggable Targets and Targeted Drugs: Enhancing the Development of New Therapeutics
Billingsley, M. L. (2008) · Pharmacology · doi:10.1159/000157624 · Cited by 49 · see citing works
Paege, N., Jung, S., SchäPe, P., Müller-Hagen, D., Ouedraogo, J., Heiderich, C., Jedamzick, J., Nitsche, B. M., van den Hondel, C. A. M. J. J., Ram, A. F. J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165755 · Cited by 50 · see citing works
Ficklin, S. P., Dunwoodie, L. J., Poehlman, W. L., Watson, C., Roche, K. E., & Feltus, F. A. (2017) · Scientific Reports · doi:10.1038/s41598-017-09094-4 · Cited by 48 · see citing works
Machine learning applications for therapeutic tasks with genomics data
Huang, K., Xiao, C., Glass, L. M., Critchlow, C. W., Gibson, G. C., & Sun, J. (2021) · Patterns · doi:10.1016/j.patter.2021.100328 · Cited by 46 · see citing works
Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network
Atias, O., Chor, B., & Chamovitz, D. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-86 · Cited by 43 · see citing works
GO-Module: functional synthesis and improved interpretation of Gene Ontology patterns
Yang, X. H., Li, J., Lee, Y., & Lussier, Y. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr142 · Cited by 43 · see citing works
Joint Bayesian variable and graph selection for regression models with network‐structured predictors
Peterson, C. B., Stingo, F. C., & Vannucci, M. (2015) · Statistics in Medicine · doi:10.1002/sim.6792 · Cited by 42 · see citing works
Ayre, D. C., Elstner, M., Smith, N. C. P., Moores, E. S., Hogan, A. M., & Christian, S. L. (2015) · Immunology · doi:10.1111/imm.12493 · Cited by 40 · see citing works
Reverter, A., Ingham, A. B., & Dalrymple, B. P. (2008) · BioData Mining · doi:10.1186/1756-0381-1-8 · Cited by 40 · see citing works
Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture
Waiho, K., Afiqah‐Aleng, N., Iryani, M. T. M., & Fazhan, H. (2020) · Reviews in Aquaculture · doi:10.1111/raq.12468 · Cited by 40 · see citing works
Prediction of Drug–Gene Interaction by Using Metapath2vec
Zhu, S., Bing, J., Min, X., Lin, C., & Zeng, X. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00248 · Cited by 39 · see citing works
- Authors
- Wolfe, C. J., Kohane, I. S., & Butte, A. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Wolfe, Cecily J
- Venue
- BMC Bioinformatics
- Published
- 2005-09-14
- Volume
- 6
- Issue
- 1
- Pages
- 227-227
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC1239911
- OpenAlex
- W2159767718
- Cited by
- 422
- References
- 24
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 12 · 2023: 14 · 2022: 28 · 2021: 39 · 2020: 40 · 2019: 29 · 2018: 24 · 2017: 19 · 2016: 17 · 2015: 20 · 2014: 18 · 2013: 20 · 2012: 20
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Pediatric Endocrine Society
- National Institutes of Health (LM008748)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK62948, K12DK63696, K12DK063696, R01DK062948)
- U.S. National Library of Medicine (U54LM008748, 5T15 LM07092, 2 TA5 LM 07092-11, T15LM007092)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- guilt by association, gene co-expression network, Gene Ontology, gene function prediction, functional modules, probabilistic scoring, transcriptome, transcriptional regulation
- MeSH terms
- Animals, Bone Development, Feeding Behavior, Humans, Phylogeny, Probability, ROC Curve, Species Specificity, Reproducibility of Results, Receptors, Neuropeptide, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Synteny, Mice, Rats
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