Huynh-Thu, Vân Anh ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Language :
English
Title :
dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data
Publication date :
21 February 2018
Journal title :
Scientific Reports
eISSN :
2045-2322
Publisher :
Nature Publishing Group, United Kingdom
Volume :
8
Pages :
3384
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE] IUAP DYSCO
Bar-Joseph, Z., Gitter, A. & Simon, I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet. 13, 552-564 (2012).
Butte, A. J. & Kohane, I. S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. on Biocomput. 5, 415-426 (2000).
Margolin, A. A. et al. ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinforma. 7, S7 (2006).
Faith, J. J. et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, e8 (2007).
Meyer, P. E., Kontos, K., Lafitte, F. & Bontempi, G. Information-theoretic inference of large transcriptional regulatory networks. EURASIP J. on Bioinforma. Syst. Biol. 2007, 79879 (2007).
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring regulatory networks from expression data using tree-based methods. PLoS One 5, e12776 (2010).
Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796-804 (2012).
Penfold, C. A. & Wild, D. L. How to infer gene networks from expression profiles, revisited. Interface Focus. 1, 857-870 (2011).
Michailidis, G. & d'Alché Buc, F. Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues. Math. Biosci. 246, 326-334 (2013).
Breiman, L. Random forests. Mach. Learn. 45, 5-32 (2001).
Bellot, P., Olsen, C., Salembier, P., Oliveras-Vergés, A. & Meyer, P. E. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinforma. 16, 312 (2015).
Zhang, X. et al. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinforma. 29, 106-113 (2013).
Klemm, S. L. Causal structure identification in non-linear dynamical systems. Master's thesis, University of Cambridge, UK (2008).
Huynh-Thu, V. A. Machine learning-based feature ranking: Statistical interpretation and gene network inference. Ph.D. thesis, University of Liège, Belgium (2012).
Marchand, G. et al. Bridging physiological and evolutionary time-scales in a gene regulatory network. New Phytol. 203, 685-696 (2014).
Bonneau, R. et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7, R36 (2006).
Greenfield, A., Madar, A., Ostrer, H. & Bonneau, R. DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models. PLoS One 5, e13397 (2010).
Greenfield, A., Hafemeister, C. & Bonneau, R. Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinforma. 29, 1060-1067 (2013).
Breiman, L., Friedman, J. H., Olsen, R. A. & Stone, C. J. Classification and Regression Trees. (Wadsworth International, California, 1984).
Huynh-Thu, V. A. & Sanguinetti, G. Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinforma. 31, 1614-1622 (2015).
Oates, C. J. & Mukherjee, S. Network inference and biological dynamics. The Annals Appl. Stat. 6, 1209-1235 (2012).
Lim, N., Senbabaoglu, Y., Michailidis, G. & d'Alché Buc, F. OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinforma. 29, 1416-1423 (2013).
Prill, R. J. et al. Towards a rigorous assessment of systems biology models: The DREAM3 challenges. PLoS One 5, e9202 (2010).
Stolovitzky, G., Monroe, D. & Califano, A. Dialogue on reverse-engineering assessment and methods: The DREAM of high-throughput pathway inference. Annals New York Acad. Sci. 1115, 11-22 (2007).
Stolovitzky, G., Prill, R. J. & Califano, A. Lessons from the DREAM2 challenges. Annals New York Acad. Sci. 1158, 159-95 (2009).
Lopes, M. & Bontempi, G. Experimental assessment of static and dynamic algorithms for gene regulation inference from time series expression data. Front. Genet. 4, 303 (2013).
Lèbre, S. Inferring dynamic bayesian networks with low order independencies. Stat. Appl. Genet. Mol. Biol. 8, Article 9 (2009).
Beal, M. J., Falciani, F., Ghahramani, Z., Rangel, C. & Wild, D. L. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinforma. 21, 349-356 (2005).
Bansal, M., Della Gatta, G. & di Bernardo, D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinforma. 22, 815-822 (2006).
Äijö, T. & Lähdesmäki, H. Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinforma. 25, 2937-2944 (2009).
Seth, A. K. A MATLAB toolbox for Granger causal connectivity analysis. J. Neurosci. Methods 186, 262-273 (2010).
Pinna, A., Soranzo, N. & de la Fuente, A. From knockouts to networks: Establishing direct cause-effect relationships through graph analysis. PLoS One 5, e12912 (2010).
Orlando, D. A. et al. Global control of cell-cycle transcription by coupled CDK and network oscillators. Nat 453, 944-947 (2008).
Geisberg, J. V., Moqtaderi, Z., Fan, X., Ozsolak, F. & Struhl, K. Global analysis of mRNA isoform half-lives reveals stabilizing and destabilizing elements in yeast. Cell 156, 812-824 (2014).
Hooper, S. D. et al. Identification of tightly regulated groups of genes during Drosophila melanogaster embryogenesis. Mol. Syst. Biol. 3, 72 (2007).
Murali, T. et al. DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic Acids Res. 39, D736-D743 (2011).
Burow, D. A. et al. Dynamic regulation of mRNA decay during neural development. Neural development 10, 11 (2015).
Jozefczuk, S. et al. Metabolomic and transcriptomic stress response of Escherichia coli. Mol. Syst. Biol. 6, 364 (2010).
Salgado, H. et al. RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Res. 41, D203-D213 (2013).
Esquerré, T. et al. Dual role of transcription and transcript stability in the regulation of gene expression in Escherichia coli cells cultured on glucose at different growth rates. Nucleic Acids Res. 42, 2460-2472 (2014).
Boulesteix, A.-L. & Slawski, M. Stability and aggregation of ranked gene lists. Briefings Bioinforma. 10, 556-568 (2009).
Rasmussen, C. & Williams, C. K. I. Gaussian Processes for Machine Learning (MIT Press, 2006).
Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46, 2496-2506 (2016).
Ocone, A., Haghverdi, L., Mueller, N. S. & Theis, F. J. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinforma. 31, i89-i96 (2015).
Petralia, F., Wang, P., Yang, J. & Tu, Z. Integrative random forest for gene regulatory network inference. Bioinforma. 31, i197-i205 (2015).
de Jong, H. Modeling and simulation of genetic regulatory systems: A literature review. J. Comput. Biol. 9, 67-103 (2002).