The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Inferring Cellular Networks Using Probabilistic Graphical Models
Abstract
High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.
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Cites
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
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000) · Molecular Biology of the Cell · doi:10.1091/mbc.11.12.4241 · Cited by 4,815
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956
Discovering regulatory and signalling circuits in molecular interaction networks
Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s233 · Cited by 1,197
Inferring gene networks from time series microarray data using dynamic Bayesian networks
Kim, S. Y. (2003) · Briefings in Bioinformatics · doi:10.1093/bib/4.3.228 · Cited by 308
Cited by
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
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131
“Probabilistic graphical models have been widely used to model GRNs [15].”
MethodSparse and Compositionally Robust Inference of Microbial Ecological Networks
Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., & Bonneau, R. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004226 · Cited by 1,799
Modelling and analysis of gene regulatory networks
Karlebach, G., & Shamir, R. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2503 · Cited by 1,296
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190
Machine learning in bioinformatics
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J. A., Armañanzas, R., Santafé, G., Pérez, A., et al. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbk007 · Cited by 867
Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq182 · Cited by 786
3D Graph Neural Networks for RGBD Semantic Segmentation
Qi, X., Liao, R., Jia, J., Fidler, S., & Urtasun, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.556 · Cited by 482
Bonn, S., Zinzen, R. P., Girardot, C., Gustafson, E. H., Perez-Gonzalez, A., Delhomme, N., Ghavi-Helm, Y., Wilczyński, B., Riddell, A., & Furlong, E. E. M. (2012) · Nature Genetics · doi:10.1038/ng.1064 · Cited by 502
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
Heterogeneity of tumor-induced gene expression changes in the human metabolic network
Hu, J., Locasale, J. W., Bielas, J. H., O'Sullivan, J., Sheahan, K., Cantley, L. C., Heiden, M. G. V., & Vitkup, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2530 · Cited by 407
Reverse engineering cellular networks
Margolin, A. A., Wang, K., Lim, W. K., Kustagi, M., Nemenman, I., & Califano, A. (2006) · Nature Protocols · doi:10.1038/nprot.2006.106 · Cited by 379
Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas
(2018) · doi:10.1016/j.celrep.2018.10.045 · Cited by 341
A primer on learning in Bayesian networks for computational biology
Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030129 · Cited by 311
Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282
Reverse engineering and identification in systems biology: strategies, perspectives and challenges
Villaverde, A. F., & Banga, J. R. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0505 · Cited by 229
Bayesian methods in bioinformatics and computational systems biology
Wilkinson, D. J. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbm007 · Cited by 230
Comparing Statistical Methods for Constructing Large Scale Gene Networks
Allen, J. D., Xie, Y., Chen, M., Girard, L., & Xiao, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029348 · Cited by 186
Modeling formalisms in Systems Biology
Machado, D., Costa, R. S., Rocha, M., Ferreira, E. C., Tidor, B., & Rocha, I. (2011) · AMB Express · doi:10.1186/2191-0855-1-45 · Cited by 189
Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt687 · Cited by 110
Integrative multi-omics module network inference with lemon-tree
Bonnet, E., Calzone, L., & Michoel, T. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003983 · Cited by 98
Efficient reverse-engineering of a developmental gene regulatory network
Crombach, A., Wotton, K. R., Cicin-Sain, D., Ashyraliyev, M., & Jaeger, J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002589 · Cited by 83
“). This approach has two significant limitations: first, spatial information on gene expression is lost, since homogenised tissue samples or disaggregated cells are studied. And second, most resulting models are of a static and probabilistic nature, which cannot be used to investigate network dynamics (for example”
BackgroundInference in Bayesian networks
Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2006) · Nature Biotechnology · doi:10.1038/nbt0106-51 · Cited by 92
Regulatory network control of blood stem cells
Göttgens, B. (2015) · Blood · doi:10.1182/blood-2014-08-570226 · Cited by 82
Doyle, F. J., & Stelling, J. (2006) · Journal of The Royal Society Interface · doi:10.1098/rsif.2006.0143 · Cited by 60
Genetic Networks Governing Heart Development
Waardenberg, A. J., Ramialison, M., Bouveret, R., & Harvey, R. P. (2014) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a013839 · Cited by 59
The center for causal discovery of biomedical knowledge from big data
Cooper, G. F., Bahar, I., Becich, M. J., Benos, P. V., Berg, J., Espino, J., Glymour, C., Jacobson, R. C., Kienholz, M., Lee, A. V., et al. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv059 · Cited by 36
Biophysically Motivated Regulatory Network Inference: Progress and Prospects
Äijö, T., & Bonneau, R. (2016) · Human Heredity · doi:10.1159/000446614 · Cited by 30
Network-Free Inference of Knockout Effects in Yeast
Peleg, T., Yosef, N., Ruppin, E., & Sharan, R. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000635 · Cited by 14
Single‐cell pluripotency regulatory networks
Stumpf, P. S., Ewing, R., & MacArthur, B. D. (2016) · PROTEOMICS · doi:10.1002/pmic.201500528 · Cited by 4
Metadata
- Authors
- Friedman, N.
- Venue
- Science
- Published
- 2004-02-06
- Volume
- 303
- Issue
- 5659
- Pages
- 799-805
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2125631472
- Cited by
- 1,202
- References
- 5
- FWCI
- 23.07
- Type
- journal article
- Language
- en
- Open access
- closed
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