Literature
(Redirected from The DREAM Project/Literature)
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Reverse Engineering Literature
- Akutsu T, Miyano S, and Kuhara S, Inferring qualitative relations in genetic networks and metabolic pathways, Bioinformatics 16(8):727-734, 2005.
- Andrec M, Kholodenko BN, Levy RM, and Sontag E, Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy, Journal of Theoretical Biology 232:427-441, 2005.
- Beal MJ, Falciani F, Ghahramani Z, Rangel C, and Wild DL, A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, Bioinformatics 21(3):349-356, 2005.
- Carlos C, and Troya JM, Reverse engineering of temporal Boolean networks from noisy data using evolutionary algorithms, Neurocomputing 62:111-129, 2004.
- de la Fuente A., Bing N., Hoeschele I., and Mendes P., Discovery of meaningful associations in genomic data using partial correlation coefficients, Bioinformatics 20(18):3565-3574, 2004.
- Friedman N., Linial M., Nachman I., Pe'er D., Using Bayesian Networks to Analyze Expression Data, Journal of Computational Biology, 2000.
- Gardner TS, di Bernardo D, Lorenz D, and Collins JJ., Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling, Science 301:102, 2003.
- Hanen BH., Masmoudi A., and Ahmed R., Causal inference in biomolecular pathways using a Bayesian network approach and an Implicit method, Journal of Theoretical Biology, 2008.
- Jarrah A., Laubenbacher R., Stigler B., and Stillman M., Reverse-engineering polynomial dynamical systems, Advances in Applied Mathematics 39:477-489, 2007.
- Kholodenko BN, Kiyatkin A, Bruggeman FJ, Sontag E, Westerhoff HV, and Hoek JB, Untangling the wires: a strategy to trace functional interactions in signaling and gene networks, Proc Natl Acad Sci (PNAS) 99(20):12841-12846, 2002.
- Kim J, Bates DG, Postlethwaite I, Heslop-Harrison P, and Cho KH., Least-squares methods for identifying biochemical regulatory networks from noisy measurements, BMC Bioinformatics 8:8, 2007.
- Kuo-Ching Liang and Xiaodong Wang, Gene Regulatory Network Reconstruction Using Conditional Mutual Information, EURASIP Journal on Bioinformatics and Systems Biology, 2008.
- Liang S, Fuhrman S, and Somogyi R, Reveal, a general reverse engineering algorithm for inference of genetic network architectures, Pac Symp Biocomput, 3:18-10, 1998.
- Liu W, Lähdesmäki H, Dougherty ER, and Shmulevich I., Inference of boolean networks using sensitivity regularization, EURASIP Journal on Bioinformatics and Systems Biology, 2008.
- Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, and Califano A., ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context", BMC Bioinformatics 7(Suppl 1):S7, 2006.
- Nariai N, Tamada Y, Imoto S, and Miyano S, Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data, Bioinformatics 21(Suppl 2):ii206-ii212, 2005.
- Pournara I., and Wernisch L.,Reconstruction of gene networks using Bayesian learning and manipulation experiments, Bioinformatics 20(17):2934-2943, 2004.
- Rao A, Hero AO, States DJ, and Engel JD., Using directed information to build biologically relevant influence networks, Journal of Bioinformatics and Computational Biology, 2008.
- Repsilber D., Liljenströmb H., and Andersson G. E., Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses, Biosystems 66:31-41, 2002.
- Rice JJ, Tu Y, Stolovitzky G., Reconstructing biological networks using conditional correlation analysis, Bioinformatics 21(6):765-773, 2005.
- Stigler B., and Laubenbacher R., A computational algebra approach to the reverse engineering of gene regulatory networks, Journal of Theoretical Biology, 2004.
- Yong Wang, Trupti Joshi, Xiang-Sun Zhang, Dong Xu, and Luonan Chen, Inferring gene regulatory networks from multiple microarray datasets, Bioinformatics 22(19):2413-2420, 2006.
- Yu H, Zhu S, Zhou B, Xue H, and Han JD, Inferring causal relationships among different histone modifications and gene expression, Genome Research, 2008.
- Yu J., Smith V., Wang P., Hartemink A., and Jarvis E., Advances to Bayesian network inference for generating causal networks from observational biological data, Bioinformatics 20(18):3594-3603, 2004.
- Zhao W, Serpedin E, and Dougherty ER, Recovering genetic regulatory networks from chromatin immunoprecipitation and steady-state microarray data, EURASIP Journal on Bioinformatics and Systems Biology, 2008.
- Zou M, and Conzen SD, A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data, Bioinformatics 21(1):71-79, 2005.
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Reviews
- J.J. Rice and G. Stolovitzky, Making the most of it: Pathway reconstruction and integrative simulation using the data at hand, Biosilico 2(2):70-7, 2004.
- G. Stolovitzky and A. Califano, Systems Biology: Making Sense of Oceans of Biological Data, NY Acad. of Sciences Update Magazine, March/April 2006.
- H.J. Bussemaker, B.C. Foat, and L.D.Ward, Modeling of Genome-Wide mRNA Expression: From Modules to Molecules, Annual Review of Biophysics and Biomolecular Structure Vol. 36: 329-347, 2007.
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General Articles
- Rice JJ, Tu Y, Stolovitzky G., Reconstructing biological networks using conditional correlation analysis, Bioinformatics, 21(6):765-73, 2005.
- Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A., Reverse engineering of regulatory networks in human B cells, Nat Genet. 37(4):382-90, 2005.
- Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A., ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinformatics 2006, 7(Suppl 1):S7, 2006.
- Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A., Reverse engineering cellular networks, Nat Protoc, 1(2):662-71, 2006.
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Reverse Engineering Links
RegulonDB, an electronically-encoded transcriptional network of E. coli.
ENFIN, an European Network of Excellence on Systems Biology. The ENFIN network is committed to provide a Europe-wide integration of computational approaches in systems biology.
