The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
How to infer gene networks from expression profiles
Open accessAbstract
Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.
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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
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 16,184
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
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117
Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S., & Thorsson, V. (2006) · Genome Biology · doi:10.1186/gb-2006-7-5-r36 · Cited by 510
Research works printed but not linked: 23
- Amato R, Ciaramella A, Deniskina N, Del Mondo C, di Bernardo D, Donalek C, Longo G, Mangano G, Miele G, Raiconi G, Staiano A, Tagliaferri R (2006) A multi‐step approach to time series analysis and gene expression clustering. Bioinformatics 22: 589–596 · DOI
- Ambesi A, di Bernardo D (2006) Computational biology and drug discovery: From single‐target to network drugs. Curr Bioinform 1: 3–13 · DOI · Paper page
- Bansal M, Della Gatta G, di Bernardo D (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22: 815–822 · DOI · Paper page
- Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla‐Favera R, Califano A (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37: 382–390 · DOI · Paper page
- Beer MA, Tavazoie S (2004) Predicting gene expression from sequence. Cell 117: 185–198 · DOI · Paper page
- Brazhnik P, de la Fuente A, Mendes P (2003) Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19 (Suppl 2): II122–II129 · DOI · Paper page
- Breitkreutz B, Stark C, Tyers M (2003) Osprey: a network visualization system. Genome Biol 4: R22.2–R22.4 · doi:10.1186/gb-2003-4-7-222
- Butte A, Kohane I (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 418–429 · DOI · Paper page
- de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comp Biol 9: 67–103 · DOI · Paper page
- D'haeseleer P, Wen X, Fuhrman S, Somogyi R (1999) Linear modeling of mrna expression levels during cns development and injury. Pac Symp Biocomput 41–52 · DOI · Paper page
- di Bernardo D, Thompson M, Gardner T, Chobot S, Eastwood E, Wojtovich A, Elliott S, Schaus S, Collins J (2005) Chemogenomic profiling on a genome‐wide scale using reverse‐engineered gene networks. Nat Biotechnol 23: 377–383 · DOI · Paper page
- Faith J, Gardner T (2005) Reverse‐engineering transcription control networks. Phys Life Rev 2: 65–88 · DOI · Paper page
- Foat B, Morozov A, Bussemaker HJ (2006) Statistical mechanical modeling of genome‐wide transcription factor occupancy data by matrixreduce. Bioinformatics 22: e141–e149 · DOI · Paper page
- Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH (2000) Functional discovery via a compendium of expression profiles. Cell 102: 109–126 · DOI · Paper page
- Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Della Favera R, Califano A (2006) Aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics S1 (arXiv: q–bio.MN/0410037) · DOI · Paper page
- Pe'er D, Nachman I, Linial M, Friedman N (2000) Using bayesian networks to analyze expression data. J Comput Biol 7: 601–620 · DOI · Paper page
- Prakash A, Tompa M (2005) Discovery of regulatory elements in vertebrates through comparative genomics. Nat Biotechnol 23: 1249–1256 · DOI
- Steuer R, Kurths J, Daub CO, Weise J, Selbig J (2002) The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18 (Suppl 2): 231–240, Evaluation Stud · DOI · Paper page
- Tadesse M, Vannucci M, Lio P (2004) Identification of DNA regulatory motifs using bayesian variable selection. Bioinformatics 20: 2556–2561 · DOI
- Tegner J, Yeung MK, Hasty J, Collins JJ (2003) Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 100: 5944–5949 · DOI · Paper page
- van Someren E, Vaes B, Steegenga W, Sijbers A, Dechering K, Reinders M (2006) Least absolute regression network analysis of the murine osteoblast differentiation network. Bioinformatics 22: 477–484 · DOI
- Workman C, Mak H, McCuine S, Tagne J, Agarwal M, Ozier O, Begley T, Samson L, T I (2006) A systems approach to mapping DNA damage response pathways. Science 312: 1054–1059 · DOI · Paper page
- Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20: 3594–3603 · DOI · Paper page
Cited by
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
“They are however also less informative for the prediction of edge directionality [3,26,27] and therefore make the regulatory network inference task more challenging.”
BackgroundPlasma MicroRNA Profiling Reveals Loss of Endothelial MiR-126 and Other MicroRNAs in Type 2 Diabetes
Zampetaki, A., Kiechl, S., Drozdov, I., Willeit, P., Mayr, U., Prokopi, M., Mayr, A., Weger, S., Oberhollenzer, F., Bonora, E., et al. (2010) · Circulation Research · doi:10.1161/circresaha.110.226357 · Cited by 1,283
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170
Gene regulatory network inference: Data integration in dynamic models—A review
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755
A survey on filter techniques for feature selection in gene expression microarray analysis
Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., & Nowe, A. (2012) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.33 · Cited by 619
Metadata
- Authors
- Bansal, M., Belcastro, V., Ambesi‐Impiombato, A., & di Bernardo, D.
- Venue
- Molecular Systems Biology
- Published
- 2007-01
- Volume
- 3
- Issue
- 1
- Pages
- 78-78
- ISSN
- 1744-4292 (ISSN-L)
- OpenAlex
- W2115108153
- Cited by
- 766
- References
- 5
- FWCI
- 25.26
- Type
- journal article
- Language
- en
- Update notices
- erratum (2007-06-26)
- Open access
- gold
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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