. .. Background-on-inverse-problems, 175 7.2.1 Brief review on image segmentation and/or restoration

. .. , 184 7.3.1 Joint multi-spectral image segmentation and deconvolution, HOGMep: application to image processing and biological data, vol.184

. .. Conclusions-on-hogmep,

, 10 2.2 Diagram of the principle of two-channel microarray technology, p.12

, Principle of the hybridization in a microarray

, Illustration of main steps of RNA-seq experiments

.. .. Genenetweaver-pipeline,

, Binary log transformation effect on RG-plot

, Lowess normalization effects on MA-plot and RG-plot

. Effects and .. .. Deseq,

. .. Gene-regulatory-mechanism, 32 2.10 Graph structure encoding a gene regulatory mechanism

, Main steps of gene regulatory network inference

, Summing-up of the main stages of genetic engineering

. .. A-bayesian-network,

. Network and . .. Underlying-boolean-functions, , p.48

, Example of a regression tree

, Experimental design for RNA-seq data

, Graph representations of a 4 × 4 image

, Image segmentation with Graph Cuts

, Image segmentation with random walker

. .. Convex,

.. .. Tf-connectivity-a-priori,

. .. , TFs cooperation mechanisms for gene expression regulation, vol.81

, Flow network construction for CT problem

.. .. Brane-cut,

, Flow network construction for BRANE Cut after dimension reduction, p.86

, PR curves for the dataset 1 of DREAM4

). .. Brane-cut, 89 4.10 PR curves for the dataset 3 of DREAM4 (BRANE Cut)

. .. Dream4, 93 4.15 PR curves for the Escherichia coli dataset (BRANE Cut)

, Range-Precision-dependent performance on Escherichia coli dataset, p.95

B. Ct and . .. Network-characteristics, , p.96

. .. Of-dream4, , p.98

. .. Of-dream4, , p.99

. .. Of-dream4, , p.99

. .. Of-dream4, , p.100

. .. Of-dream4, , p.100

. .. , Circuit design for the search of differentially expressed genes, vol.104

, DE genes of Rut-C30 on various mixing of carbon sources

. .. , Median profiles of the five clusters obtained from 650 DE genes, p.105

. .. , PR curves for the dataset 1 of DREAM4 (q-BRANE Relax), p.120

. .. , PR curves for the dataset 2 of DREAM4 (q-BRANE Relax), p.120

. .. , PR curves for the dataset 3 of DREAM4 (q-BRANE Relax), p.121

. .. , 6 PR curves for the dataset 4 of DREAM4 (q-BRANE Relax), p.121

. .. , PR curves for the dataset 5 of DREAM4 (q-BRANE Relax), p.122

, Huber function for various ? parameters

. .. , 125 5.10 Convergence profiles for various algorithms solving BRANE Relax, p.127

, Convergence time dependence on block size for BC-P-FB implementation of BRANE Relax

). .. Relax, 129 5.13 PR curves for the dataset 2 of DREAM4 (h-BRANE Relax), 130 5.16 PR curves for the dataset 5 of DREAM4 (h-BRANE Relax)

, hard -clustering effect on network inference

, Graph interpretation for BRANE Clust with hard -clustering, p.209

. .. , PR curves for the dataset 1 of DREAM4 (BRANE Clust-hard ), p.140

. .. , PR curves for the dataset 2 of DREAM4 (BRANE Clust-hard ), p.141

. .. , PR curves for the dataset 3 of DREAM4 (BRANE Clust-hard ), p.141

. .. , PR curves for the dataset 4 of DREAM4 (BRANE Clust-hard ), p.142

. .. , PR curves for the dataset 5 of DREAM4 (BRANE Clust-hard ), p.142

. .. , 146 6.10 Graph construction for hard and soft-clustering, PR curves for the dataset 1 of DREAM5 (BRANE Clust-hard )

. .. , 149 6.12 PR curves for the dataset 1 of DREAM4 (BRANE Clust-soft)

. .. , 154 6.18 PR curves for the dataset 1 of DREAM5 (BRANE Clust-soft), F -plots for the dataset 2 of DREAM4 (BRANE Clust-soft), p.154

, F -plots for the dataset 1 of DREAM5 (BRANE Clust-soft)

. .. , 156 6.22 CT and BRANE Clust Escherichia coli network characteristics, F -plots for the dataset 3 of DREAM5 (BRANE Clust-soft), p.157

, Intrinsic clustering evaluation of BRANE Clust

. .. Of-dream4, , p.161

. .. Of-dream4, , p.161

. .. Of-dream4, , p.162

. .. Of-dream4, , p.162

. .. Of-dream4, , p.163

, F -plots for the dataset 1 of DREAM4 (BRANE Clust-soft)

, F -plots for the dataset 2 of DREAM4 (BRANE Clust-soft)

, F -plots for the dataset 3 of DREAM4 (BRANE Clust-soft)

, F -plots for the dataset 4 of DREAM4 (BRANE Clust-soft)

, F -plots for the dataset 5 of DREAM4 (BRANE Clust-soft)

, Scheme of linear modeling with additive noise

M. Power,

, Dependency relationships between variables in HOGMep

. .. , Restoration results with noise level set to ? = 0.01 ('Synth'), p.186

. .. , Restoration results with noise level set to ? = 0.05 ('Synth'), p.187

. .. , Restoration results with noise level set to ? = 0.1 ('Synth'), p.188

. .. , Restoration results with noise level set to ? = 0.01 ('Peppers'), p.189

. .. , Restoration results with noise level set to ? = 1 ('Peppers'), p.190

. .. , Segmentation results with noise level set to ? = 0.01 ('Synth'), p.192

. .. , Segmentation results with noise level set to ? = 0.05 ('Synth'), p.193

. .. , Segmentation results with noise level set to ? = 0.1 ('Synth'), p.194

. .. , Segmentation results with noise level set to ? = 0.01 ('Peppers'), p.194

. .. , Segmentation results with noise level set to ? = 1 ('Peppers')

. .. , Histogram of silhouette for noise variance ? = 0.01 ('Peppers')

. .. , Histogram of silhouette for noise variance ? = 1 ('Peppers')

, Segmentation results for the breast cancer dataset

.. .. ,

.. .. Characteristics,

, Splitting scheme of the node-dependent ? i

. .. , Numerical performance on the dataset 1 of DREAM5 (BRANE Cut), p.93

. .. , Numerical performance on Escherichia coli dataset (BRANE Cut), p.94

, Significant STRING scores for BRANE Cut predictions

, Numerical performance on DREAM4 (BRANE Relax)

. .. , Post-processing performance on DREAM4 (BRANE Relax), p.123

, Numerical performance on DREAM5 (BRANE Relax)

. .. , Numerical performance on DREAM4 (BRANE Clust-hard ), p.143

. .. , Post-processing performance on DREAM4 (BRANE Clust-hard ), p.144

, Numerical performance on the dataset 1 of DREAM5 (BRANE Clust-hard ), p.144

. .. , Numerical performance on DREAM4 (BRANE Clust-soft), p.152

. .. , Post-processing performance on DREAM4 (BRANE Clust-soft), p.153

. .. , Numerical performance on the dataset 1 of DREAM5 (BRANE Clust-soft), p.153

, Numerical performance of BRANE Clust on the Escherichia coli dataset, p.156

, Significant STRING scores for BRANE Cut predictions

. .. Brane-clust, , p.160

. .. , Channel and color restoration results in terms of SNR ('Synth'), p.191

. .. , Channel and color restoration results in terms of SNR ('Peppers'), p.191

, Segmentation results in terms of VI ('Synth')

. .. , Segmentation results in terms of silhouette ('Peppers'), p.195

. .. , Numerical performance for breast cancer data classification

D. Abdulrehman, P. T. Monteiro, M. C. Teixeira, N. P. Mira, A. B. Lourenço et al., YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface, Nucleic Acids Res, vol.39, pp.136-140, 2011.

S. Aerts, G. Thijs, B. Coessens, M. Staes, Y. Moreau et al., Toucan: deciphering the cis-regulatory logic of coregulated genes, Nucleic Acids Res, vol.31, issue.6, pp.1753-1764, 2003.

S. Aerts, P. Van-loo, G. Thijs, H. Mayer, R. De-martin et al., TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis, Nucleic Acids Res, vol.33, pp.393-396, 2005.

H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Contr, vol.19, issue.6, pp.716-723, 1974.

T. Akutsu, S. Miyano, and S. Kuhara, Identification of genetic networks from a small number of gene expression patterns under the Boolean network model, In Pac. Symp. Biocomput, vol.4, pp.17-28, 1999.

G. Altay and F. Emmert-streib, Inferring the conservative causal core of gene regulatory networks, BMC Syst. Biol, vol.4, issue.1, p.132, 2010.

C. Ambroise, J. Chiquet, M. , and C. , Inferring sparse Gaussian graphical models with latent structure, Electron. J. Stat, vol.3, pp.205-238, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00592201

S. Anders and W. Huber, Differential expression analysis for sequence count data, Genome Biol, vol.11, issue.10, p.106, 2010.

J. Angulo and J. Serra, Automatic analysis of DNA microarray images using mathematical morphology, Bioinformatics, vol.19, issue.5, pp.553-562, 2003.

F. J. Anscombe, The transformation of Poisson, binomial and negative-binomial data, Biometrika, vol.35, issue.3/4, pp.246-254, 1948.

S. M. Arfin, A. D. Long, E. T. Ito, L. Tolleri, M. M. Riehle et al., Global gene expression profiling in Escherichia coli K12. The effects of integration host factor, J. Biol. Chem, vol.275, issue.38, pp.29672-29684, 2000.

N. Aro, A. Saloheimo, M. Ilmen, and M. Penttila, ACEII, a novel transcriptional activator involved in regulation of cellulase and xylanase genes of Trichoderma reesei, J. Biol. Chem, vol.276, issue.26, pp.24309-24314, 2001.

M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler et al., Gene Ontology: tool for the unification of biology, Nat. Genet, vol.25, issue.1, pp.25-29, 2000.

P. L. Auer and R. W. Doerge, A two-stage Poisson model for testing RNA-seq data, Stat. Appl. Genet. Mol. Biol, vol.10, issue.1, 2011.

H. Ayasso and A. Mohammad-djafari, Joint NDT image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation, IEEE Trans. Image Process, vol.19, issue.9, pp.2265-2277, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00494945

C. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara, and K. M. Borgwardt, Efficient network-guided multi-locus association mapping with graph cuts, Bioinformatics, vol.29, issue.13, pp.171-179, 2013.

S. D. Babacan, R. Molina, and A. K. Katsaggelos, Sparse Bayesian image restoration, Proc. Int. Conf. Image Process, 2010.

S. D. Babacan, R. Molina, and A. K. Katsaggelos, Variational Bayesian super resolution, IEEE Trans. Image Process, vol.20, issue.4, pp.984-999, 2011.

T. L. Bailey, N. Williams, C. Misleh, and W. W. Li, MEME: discovering and analyzing DNA and protein sequence motifs, Nucleic Acids Res, vol.34, pp.369-373, 2006.

D. Baird, P. Johnstone, W. , and T. , Normalization of microarray data using a spatial mixed model analysis which includes splines, Bioinformatics, vol.20, issue.17, pp.3196-3205, 2004.

S. Balaji, M. M. Babu, L. M. Iyer, N. M. Luscombe, A. et al., Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast, J. Mol. Biol, vol.360, issue.1, pp.213-227, 2006.

P. Baldi and A. D. Long, A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes, Bioinformatics, vol.17, issue.6, pp.509-519, 2001.

M. Bansal, V. Belcastro, A. Ambesi-impiombato, and D. Bernardo, How to infer gene networks from expression profiles, Mol. Syst. Biol, p.3, 2007.

M. S. Bartlett, The use of transformations, Bibliography, vol.3, issue.1, pp.39-52, 1947.

H. H. Bauschke and P. L. Combettes, Convex analysis and monotone operator theory in Hilbert spaces. CMS books in mathematics, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00643354

A. Beck and M. Teboulle, Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, IEEE Trans. Image Process, vol.18, issue.11, pp.2419-2434, 2009.

J. Beirlant, E. J. Dudewicz, L. Györfi, and E. C. Van-der-meulen, Nonparametric entropy estimation: An overview, Int. J. Math. Stat. Sci, vol.6, pp.17-39, 1997.

P. Bellot, C. Olsen, P. Salembier, A. Oliveras-vergés, and P. E. Meyer, NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference, BMC Bioinformatics, p.16, 2015.

A. Ben-dor, R. Shamir, Y. , and Z. , Clustering gene expression patterns, J. Comput. Biol, vol.6, issue.3-4, pp.281-297, 1999.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.57, issue.1, pp.289-300, 1995.

C. Berge, Graphs and hypergraphs, 1973.

J. A. Berger, S. Hautaniemi, A. Järvinen, H. Edgren, S. K. Mitra et al., Optimized LOWESS normalization parameter selection for DNA microarray data, BMC Bioinformatics, 2004.

J. M. Bernardo and A. F. Smith, , 1994.

J. Besag, On the statistical analysis of dirty pictures, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.48, issue.3, pp.259-302, 1986.

S. Beucher and C. Lantuéjoul, Use of watersheds in contour detection, Proc. Int. workshop image processing, 1979.

J. Bioucas-dias, Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors, IEEE Trans. Image Process, vol.15, issue.4, pp.937-951, 2006.

J. Bioucas-dias, F. Condessa, and J. Kova?evi´kova?evi´c, Alternating direction optimization for image segmentation using hidden Markov measure field models, Proc. SPIE Image Process. Algorithms Syst, 2014.

M. F. Blasi, I. Casorelli, A. Colosimo, F. S. Blasi, M. Bignami et al., A recursive network approach can identify constitutive regulatory circuits in gene expression data, Phys. Stat. Mech. Appl, vol.348, pp.349-370, 2005.

B. M. Bibliography-bolstad, R. A. Irizarry, M. Astrand, and T. P. Speed, A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics, vol.19, issue.2, pp.185-193, 2003.

A. Bondy and U. S. Murty, Graph Theory, 2007.

R. Bonneau, D. Reiss, P. Shannon, M. Facciotti, L. Hood et al., The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo, Genome Biol, vol.7, issue.5, p.36, 2006.

Y. Boykov and M. Jolly, Interactive organ segmentation using graph cuts, Proc. Medical Image Computing Computer-Assisted Intervention Conf, pp.276-286, 2000.

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision, IEEE Trans. Pattern Anal. Mach. Intell, vol.26, issue.9, pp.1124-1137, 2004.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Trans. Pattern Anal. Mach. Intell, vol.23, issue.11, pp.1222-1239, 2001.

L. Breiman, Random forests, Mach. Learn, vol.45, issue.1, pp.5-32, 2001.

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1984.

L. M. Briceño-arias, P. L. Combettes, J. Pesquet, and N. Pustelnik, Proximal algorithms for multicomponent image processing, J. Math. Imaging Vision, vol.41, issue.1, pp.3-22, 2011.

J. H. Bullard, E. Purdom, K. D. Hansen, and S. Dudoit, Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments, BMC Bioinformatics, vol.11, issue.1, p.94, 2010.

A. J. Butte and I. S. Kohane, Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements, 2000.

L. Dunker, K. Hunter, T. E. Lauderdale, and . Klein, Pac. Symp. Biocomput, vol.5, pp.415-429

X. Cai, Variational image segmentation model coupled with image restoration achievements, Pattern Recogn, vol.48, issue.6, pp.2029-2042, 2015.

X. Cai, R. Chan, and T. Zeng, A two-stage image segmentation method using a convex variant of the Mumford-Shah model and thresholding, SIAM J. Imaging Sci, vol.6, issue.1, pp.368-390, 2013.

M. J. Callow, S. Dudoit, E. L. Gong, T. P. Speed, and E. M. Rubin, Microarray expression profiling identifies genes with altered expression in HDL-deficient mice, 2000.

, Genome Res, vol.10, issue.12, pp.2022-2029

, Bibliography 217

J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell, vol.8, issue.6, pp.679-698, 1986.

G. Casella and R. L. Berger, Statistical Inference. Duxbury, 2002.

L. Chaâri, N. Pustelnik, C. Chaux, and J. Pesquet, Solving inverse problems with overcomplete transforms and convex optimization techniques, Proc. SPIE, Wavelets, vol.7446, 2009.

L. Chaari, F. Forbes, T. Vincent, M. Dojat, and P. Ciuciu, Variational solution to the joint detection estimation of brain activity in fMRI, Proc. Medical Image Computing Computer-Assisted Intervention Conf., volume, vol.6892, pp.260-268, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00635384

L. E. Chai, S. K. Loh, S. T. Low, M. S. Mohamad, S. Deris et al., A review on the computational approaches for gene regulatory network construction, Comput. Biol. Med, vol.48, pp.55-65, 2014.

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imaging Vision, vol.40, issue.1, pp.120-145, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00490826

G. Chantas, N. Galatsanos, A. Likas, and M. Saunders, Variational Bayesian image restoration based on a product of t-distributions image prior, IEEE Trans. Image Process, vol.17, issue.10, pp.1795-1805, 2008.

C. Charbonnier, J. Chiquet, and C. Ambroise, Weighted-Lasso for structured network inference from time course data, Stat. Appl. Genet. Mol. Biol, vol.9, issue.1, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01597614

C. Chaux, P. L. Combettes, J. Pesquet, and V. R. Wajs, A variational formulation for frame based inverse problems, Inverse Problems, vol.23, issue.4, pp.1495-1518, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00621883

C. Chaux, L. Duval, A. Benazza-benyahia, and J. Pesquet, A nonlinear Stein based estimator for multichannel image denoising, IEEE Trans. Signal Process, vol.56, issue.8, pp.3855-3870, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00617318

C. Chaux, A. Benazza-benyahia, J. Pesquet, and L. Duval, Wavelet transform for the denoising of multivariate images, Multivariate Image Processing, pp.203-238, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00621943

T. Chen, H. L. He, and G. M. Church, Modeling gene expression with differential equations, In Pac. Symp. Biocomput, 1999.

Y. Chen, E. R. Dougherty, and M. L. Bittner, Ratio-based decisions and the quantitative analysis of cDNA microarray images, J. Biomed. Opt, vol.2, issue.4, p.364, 1997.

Y. Chen, Y. Li, R. Narayan, A. Subramanian, and X. Xie, Gene expression inference with deep learning, Bioinformatics, vol.32, issue.12, pp.1832-1839, 2016.
DOI : 10.1093/bioinformatics/btw074
URL : http://europepmc.org/articles/pmc4908320?pdf=render

Z. Chen, S. D. Babacan, R. Molina, and A. K. Katsaggelos, Variational Bayesian methods for multimedia problems, IEEE Trans. Multimedia, vol.16, issue.4, pp.1000-1017, 2014.
DOI : 10.1109/tmm.2014.2307692

H. D. Cheng, X. H. Jiang, Y. Sun, W. , and J. , Color image segmentation: advances and prospects, Pattern Recogn, vol.34, issue.12, pp.2259-2281, 2001.
DOI : 10.1016/s0031-3203(00)00149-7

J. M. Cherry, E. L. Hong, C. Amundsen, R. Balakrishnan, G. Binkley et al., Saccharomyces Genome Database: the genomics resource of budding yeast, Nucleic Acids Res, vol.40, pp.700-705, 2012.

G. Chierchia, N. Pustelnik, B. Pesquet-popescu, and J. Pesquet, A nonlocal structure tensor-based approach for multicomponent image recovery problems, IEEE Trans. Image Process, vol.23, issue.12, pp.5531-5544, 2014.
DOI : 10.1109/tip.2014.2364141
URL : https://hal.archives-ouvertes.fr/hal-01372564

J. Chiquet, Contributions to Sparse Methods for Complex Data Analysis. Habilitationàtationà diriger des recherches (HDR), 2015.
URL : https://hal.archives-ouvertes.fr/tel-01288976

J. Chiquet, A. Smith, G. Grasseau, C. Matias, and C. Ambroise, SIMoNe: Statistical Inference for MOdular NEtworks, Bioinformatics, vol.25, issue.3, pp.417-418, 2009.
DOI : 10.1093/bioinformatics/btn637
URL : https://hal.archives-ouvertes.fr/hal-00592218

J. Chiquet, Y. Grandvalet, and C. Charbonnier, Sparsity in sign-coherent groups of variables via the cooperative-lasso, Ann. Appl. Stat, vol.6, issue.2, pp.795-830, 2012.

R. A. Choudrey, Variational Methods for Bayesian Independent Component Analysis, 2002.

E. Chouzenoux, J. Idier, and S. Moussaoui, A majorize-minimize strategy for subspace optimization applied to image restoration, IEEE Trans. Image Process, vol.20, issue.6, pp.1517-1528, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00516585

E. Chouzenoux, A. Jezierska, J. Pesquet, T. , and H. , A majorize-minimize subspace approach for ? 2 -? 0 image regularization, SIAM J. Imaging Sci, vol.6, issue.1, pp.563-591, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00789962

E. Chouzenoux, J. Pesquet, and A. Repetti, Variable metric forwardbackward algorithm for minimizing the sum of a differentiable function and a convex function, J. Optim. Theory Appl, vol.162, issue.1, pp.107-132, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00789970

E. Chouzenoux, J. Pesquet, and A. Repetti, A block coordinate variable metric forward-backward algorithm, J. Global Optim, vol.66, issue.3, pp.457-485, 2016.
DOI : 10.1007/s10898-016-0405-9
URL : https://hal.archives-ouvertes.fr/hal-00945918

A. Clauset, C. R. Shalizi, and M. E. Newman, Power-law distributions in empirical data, SIAM Rev, vol.51, issue.4, pp.661-703, 2009.

, Bibliography 219

W. S. Cleveland, Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc, vol.74, issue.368, pp.829-836, 1979.
DOI : 10.2307/2286407

P. L. Combettes and J. Pesquet, Proximal splitting methods in signal processing, Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.
DOI : 10.1007/978-1-4419-9569-8_10
URL : https://hal.archives-ouvertes.fr/hal-00643807

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Model. Simul, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090
URL : https://hal.archives-ouvertes.fr/hal-00017649

S. T. Coradetti, J. P. Craig, Y. Xiong, T. Shock, C. Tian et al., Conserved and essential transcription factors for cellulase gene expression in ascomycete fungi, Proc. Nat. Acad. Sci. U.S.A, vol.109, issue.19, pp.7397-7402, 2012.

C. Couprie, L. Grady, L. Najman, T. , and H. , Power watershed: A unifying graph-based optimization framework, IEEE Trans. Pattern Anal. Mach. Intell, vol.33, issue.7, pp.1384-1399, 2011.

M. J. Cowley, M. Pinese, K. S. Kassahn, N. Waddell, J. V. Pearson et al., PINA v2.0: mining interactome modules, Nucleic Acids Res, vol.40, issue.D1, pp.862-865, 2011.

F. Crick, Central dogma of molecular biology, Nature, vol.227, issue.5258, pp.561-563, 1970.

X. Cui and G. A. Churchill, Statistical tests for differential expression in cDNA microarray experiments, Genome Biol, vol.4, issue.4, p.210, 2003.

J. Darbon, Global optimization for first order Markov Random Fields with submodular priors, Discrete Appl. Math, vol.157, issue.16, pp.3412-3423, 2009.

C. O. Daub, R. Steuer, J. Selbig, and S. Kloska, Estimating mutual information using B-spline functions-an improved similarity measure for analysing gene expression data, BMC Bioinformatics, vol.5, issue.1, p.118, 2004.

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, Proc. Int. Conf. Mach. Learn, 2006.

H. De-jong, Modeling and simulation of genetic regulatory systems: A literature review, J. Comput. Biol, vol.9, issue.1, pp.67-103, 2002.

D. Smet, R. Marchal, and K. , Advantages and limitations of current network inference methods, Nat. Rev. Microbiol, vol.8, issue.10, pp.717-729, 2010.

M. C. De-souto, I. G. Costa, D. S. De-araujo, T. B. Ludermir, and A. Schliep, Clustering cancer gene expression data: a comparative study, BMC Bioinformatics, vol.9, issue.1, p.497, 2008.

A. P. Dempster, Covariance selection, Biometrics, vol.28, issue.1, pp.157-175, 1972.

J. A. Denton and J. M. Kelly, Disruption of Trichoderma reesei cre2, encoding an ubiquitin c-terminal hydrolase, results in increased cellulase activity, BMC Biotechnol, vol.11, issue.1, p.103, 2011.

E. W. Dijkstra, A note on two problems in connection with graphs, Numer. Math, vol.1, pp.269-271, 1959.

M. Dillies, A. Rau, J. Aubert, C. Hennequet-antier, M. Jeanmougin et al., A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis, Brief. Bioinform, vol.14, issue.6, pp.671-683, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01521274

C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data, J. Bioinformatics Comput. Biol, vol.3, issue.2, pp.185-205, 2005.

N. Dojer, A. Gambin, and J. Tiuryn, Applying dynamic Bayesian networks to perturbed gene expression data, BMC Bioinformatics, vol.7, p.249, 2006.

X. Dong, P. Frossard, P. Vandergheynst, and N. Nefedov, Clustering with multilayer graphs: A spectral perspective, IEEE Trans. Signal Process, vol.60, issue.11, pp.5820-5831, 2012.

X. Dong, D. Thanou, P. Frossard, and P. Vandergheynst, Learning Laplacian matrix in smooth graph signal representations, IEEE Trans. Signal Process, vol.64, issue.23, pp.6160-6173, 2016.

D. L. Donoho, M. Elad, and V. N. Temlyakov, Stable recovery of sparse overcomplete representations in the presence of noise, IEEE Trans. Inform. Theory, vol.52, issue.1, pp.6-18, 2006.

E. R. Dougherty, I. Shmulevich, J. Chen, and W. , Genomic Signal Processing and Statistics, Z. J, 2005.

S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed, Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, Statist. Sinica, vol.12, pp.111-139, 2002.

O. J. Dunn, Estimation of the medians for dependent variables, Ann. Math. Statist, vol.30, issue.1, pp.192-197, 1959.

O. J. Dunn, Multiple comparisons among means, J. Am. Stat. Assoc, vol.56, issue.293, pp.52-64, 1961.

F. Dupé, J. M. Fadili, and J. Starck, A proximal iteration for deconvolving poisson noisy images using sparse representations, IEEE Trans. Image Process, vol.18, issue.2, pp.310-321, 2009.

, Bibliography 221

B. P. Durbin, J. S. Hardin, D. M. Hawkins, and D. M. Rocke, A variance-stabilizing transformation for gene-expression microarray data, Bioinformatics, vol.18, pp.105-110, 2002.

J. Edmonds and R. M. Karp, Theoretical improvements in algorithmic efficiency for network flow problems, J. ACM, vol.19, issue.2, pp.248-264, 1972.

B. Efron, Bootstrap methods: Another look at the jackknife, Ann. Statist, vol.7, issue.1, pp.1-26, 1979.

B. Efron, Estimating the error rate of a prediction rule: Improvement on crossvalidation, J. Am. Stat. Assoc, vol.78, issue.382, pp.316-331, 1983.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann. Statist, vol.32, issue.2, pp.407-499, 2004.

E. Eisenberg and E. Y. Levanon, Human housekeeping genes, revisited, Trends Genet, vol.29, issue.10, pp.569-574, 2013.

I. A. Elbakri and J. A. Fessler, Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography with experimental validation, Phys. Med. Biol, vol.48, issue.15, pp.2453-2477, 2003.

F. Emmert-streib, G. V. Glazko, G. Altay, . De-matos, and R. Simoes, Statistical inference and reverse engineering of gene regulatory networks from observational expression data, Front. Genet, p.3, 2012.

C. Espinosa-soto and A. Wagner, Specialization can drive the evolution of modularity, PLoS Comput. Biol, vol.6, issue.3, p.1000719, 2010.

C. Evans, J. Hardin, and D. M. Stoebel, Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions, Brief. Bioinform, pp.1-17, 2017.

J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski et al., Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol, vol.5, issue.1, pp.54-66, 2007.

J. Fan, Y. Liao, and H. Liu, An overview of the estimation of large covariance and precision matrices, Econom. J, vol.19, issue.1, pp.1-32, 2016.

S. Feizi, D. Marbach, M. Médard, and M. Kellis, Network deconvolution as a general method to distinguish direct dependencies in networks, Nat. Biotechnol, vol.31, issue.8, pp.726-733, 2013.

M. A. Figueiredo, An EM algorithm for wavelet-based image restoration, IEEE Trans. Image Process, vol.12, issue.8, pp.906-916, 2003.

V. Bibliography-filkov, Identifying gene regulatory networks from gene expression data, Handbook of Computational Molecular Biology, Computer & Information Science Series, 2005.

J. Ford, L. R. Fulkerson, and D. R. , Maximal flow through a network, Canad. J. Math, vol.8, pp.399-404, 1956.

P. K. Foreman, D. Brown, L. Dankmeyer, R. Dean, S. Diener et al., Transcriptional regulation of biomass-degrading enzymes in the filamentous fungus Trichoderma reesei, J. Biol. Chem, p.278, 2003.

S. Fortunato, Community detection in graphs, Phys. Rep, vol.486, pp.75-174, 2010.

A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Res, vol.41, pp.808-815, 2013.

N. Friedman, M. Linial, I. Nachman, and D. Pe'er, Using Bayesian networks to analyze expression data, J. Comput. Biol, vol.7, issue.3-4, pp.601-620, 2000.

W. J. Fu, Penalized regressions: The bridge versus the lasso, J. Comput. Graph. Stat, vol.7, issue.3, pp.397-416, 1998.

A. Fujita, J. R. Sato, L. De-oliveira-rodrigues, C. E. Ferreira, and M. C. Sogayar, Evaluating different methods of microarray data normalization, BMC Bioinformatics, vol.7, issue.1, p.469, 2006.

F. Gadaleta, Are we far from correctly inferring gene interaction networks with Lasso?, 2015.

S. Gama-castro, V. Jiménez-jacinto, M. Peralta-gil, A. Santos-zavaleta, M. I. Peñaloza-spinola et al., RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation, Nucleic Acids Res, vol.36, pp.120-124, 2008.

S. Gama-castro, H. Salgado, M. Peralta-gil, A. Santos-zavaleta, L. Muñiz-rascado et al., , p.223

L. Olvera, R. Grande, E. Morett, and J. Collado-vides, RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (gensor units), Nucleic Acids Res, vol.39, pp.98-105, 2011.

S. Gama-castro, H. Salgado, A. Santos-zavaleta, D. Ledezma-tejeida, L. Muñiz-rascado et al., RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond, Nucleic Acids Res, vol.44, issue.D1, pp.133-143, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01460125

P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Mach. Learn, vol.63, issue.1, pp.3-42, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00341932

M. Gierli´nskigierli´nski, C. Cole, P. Schofield, N. J. Schurch, A. Sherstnev et al., Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment, Bioinformatics, issue.22, pp.3625-3630, 2015.

G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Model. Simul, vol.7, issue.3, pp.1005-1028, 2009.

A. V. Goldberg and R. E. Tarjan, A new approach to the maximum flow problem, Proc. ACM Symp. Theor. Comput, pp.136-146, 1986.

E. Gómez, M. A. Gómez-villegas, and J. M. Marín, A multivariate generalization of the power exponential family of distributions, Commun. Stat. Theory Methods, vol.27, issue.3, pp.589-600, 1998.

E. Gómez-sánchez-manzano, M. A. Gómez-villegas, and J. M. Marín, Multivariate exponential power distributions as mixtures of normal distributions with Bayesian applications, Commun. Stat. Theory Methods, vol.37, issue.6, pp.972-985, 2008.

L. Grady, Random walks for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.28, issue.11, pp.1768-1783, 2006.

L. J. Grady and J. R. Polimeni, Discrete calculus: Applied analysis on graphs for computational science, 2010.

S. Grossetête, B. Labedan, and O. Lespinet, FUNGIpath: a tool to assess fungal metabolic pathways predicted by orthology, BMC Genom, vol.11, issue.1, p.81, 2010.

F. Günther, N. Wawro, and K. Bammann, Neural networks for modeling genegene interactions in association studies, BMC Genet, vol.10, issue.1, p.87, 2009.

M. J. Ha, V. Baladandayuthapani, and K. Do, DINGO: differential network analysis in genomics, Bioinformatics, issue.21, pp.3413-3420, 2015.

J. Hadamard, Sur lesprobì emes aux dérivées partielles et leur signification physique, vol.13, pp.49-52, 1902.

M. Häkkinen, M. J. Valkonen, A. Westerholm-parvinen, N. Aro, M. Arvas et al., Screening of candidate regulators for cellulase and hemicellulase production in Trichoderma reesei and identification of a factor essential for cellulase production, Biotechnol. Biofuels, vol.7, issue.1, p.14, 2014.

A. Halleran, S. Clamons, and M. Saha, Transcriptomic characterization of an infection of Mycobacterium smegmatis by the cluster A4 mycobacteriophage Kampy, PLoS One, vol.10, issue.10, p.141100, 2015.

T. J. Hardcastle and K. A. Kelly, baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data, BMC Bioinformatics, vol.11, issue.1, pp.1-14, 2010.

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2013.

T. Hastie, R. Tibshirani, W. , and M. , Statistical Learning with Sparsity: The Lasso and Generalizations, 2015.

A. Haury, F. Mordelet, P. Vera-licona, and J. Vert, TIGRESS: Trustful Inference of Gene REgulation using Stability Selection, BMC Syst. Biol, vol.6, issue.1, p.145, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00694218

Y. He, M. Y. Hussaini, J. Ma, B. Shafei, and G. Steidl, A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data, Pattern Recogn, vol.45, issue.9, pp.3463-3471, 2012.

M. Hecker, S. Lambeck, S. Toepfer, E. Van-someren, and R. Guthke, Gene regulatory network inference: Data integration in dynamic models-a review, BioSystems, vol.96, issue.1, pp.86-103, 2009.

D. Heckerman, D. Geiger, and D. M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data, Mach. Learn, vol.20, issue.3, pp.197-243, 1995.

K. R. Hess, K. Anderson, W. F. Symmans, V. Valero, N. Ibrahim et al., , 2006.

, Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer, J. Clin. Oncol, vol.24, issue.26, pp.4236-4244

S. C. Hicks and R. A. Irizarry, quantro: a data-driven approach to guide the choice of an appropriate normalization method, Genome Biol, vol.16, issue.1, p.117, 2015.

K. Horimoto and H. Toh, Statistical estimation of cluster boundaries in gene expression profile data, Bioinformatics, vol.17, issue.12, pp.1143-1151, 2001.

, Bibliography 225

E. Howe, K. Holton, S. Nair, D. Schlauch, R. Sinha et al., MeV: MultiExperiment Viewer, Biomedical Informatics for Cancer Research, pp.267-277, 2010.

Z. Hu, P. J. Killion, and V. R. Iyer, Genetic reconstruction of a functional transcriptional regulatory network, Nat. Genet, vol.39, issue.5, pp.683-687, 2007.

P. J. Huber, Robust estimation of a location parameter, Ann. Math. Statist, vol.35, issue.1, pp.73-101, 1964.

P. J. Huber and E. M. Ronchetti, Robust statistics, 2009.

W. Huber, A. Von-heydebreck, H. Sultmann, A. Poustka, and M. Vingron, Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics, vol.18, pp.96-104, 2002.

L. Hubert and P. Arabie, Comparing partitions, J. Classif, vol.2, issue.1, pp.193-218, 1985.

D. R. Hunter and K. Lange, A tutorial on MM algorithms, Am. Stat, vol.58, issue.1, pp.30-37, 2004.

V. A. Huynh-thu and G. Sanguinetti, Combining tree-based and dynamical systems for the inference of gene regulatory networks, Bioinformatics, issue.10, pp.1614-1622, 2015.

V. A. Huynh-thu, A. Irrthum, L. Wehenkel, and P. Geurts, Inferring regulatory networks from expression data using tree-based methods, PLoS One, vol.5, issue.9, p.12776, 2010.

T. Ideker, V. Thorsson, and R. Karp, Discovery of regulatory interactions through perturbation: inference and experimental design, In Pac. Symp. Biocomput, vol.5, pp.302-313, 2000.

A. J. Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, 2008.

A. K. Jain and R. C. Dubes, Algorithms for clustering data, 1988.

A. Jezierska, E. Chouzenoux, J. Pesquet, T. , and H. , A primal-dual proximal splitting approach for restoring data corrupted with Poisson-Gaussian noise, Proc. Int. Conf. Acoust. Speech Signal Process, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00687634

M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, An introduction to variational methods for graphical models, Mach. Learn, vol.37, issue.2, pp.183-233, 1999.

A. Joshi, R. De-smet, K. Marchal, Y. Van-de-peer, and T. Michoel, Module networks revisited: computational assessment and prioritization of model predictions, Bioinformatics, vol.25, issue.4, pp.490-496, 2009.

E. Bibliography-jourdier, C. Cohen, L. Poughon, C. Larroche, F. Monot et al., Cellulase activity mapping of Trichoderma reesei cultivated in sugar mixtures under fed-batch conditions, Biotechnol. Biofuels, vol.6, issue.1, p.79, 2013.

L. Kaderali and N. Radde, Inferring gene regulatory networks from expression data, Computational Intelligence in Bioinformatics, pp.33-74, 2008.

M. Kanehisa and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Res, vol.28, issue.1, pp.27-30, 2000.

S. Kauffman, Homeostasis and differentiation in random genetic control networks, Nature, vol.224, issue.5215, pp.177-178, 1969.

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, 2005.

M. K. Kerr, M. Martin, and G. A. Churchill, Analysis of variance for gene expression microarray data, J. Comput. Biol, vol.7, issue.6, pp.819-837, 2000.

I. M. Keseler, A. Mackie, M. Peralta-gil, A. Santos-zavaleta, S. Gama-castro et al., EcoCyc: fusing model organism databases with systems biology, Nucleic Acids Res, vol.41, issue.D1, pp.605-612, 2013.

P. V. Kharchenko, L. Silberstein, and D. T. Scadden, Bayesian approach to single-cell differential expression analysis, Nat. Meth, vol.11, issue.7, pp.740-742, 2014.

Y. Kim, H. Choi, and H. Oh, Smoothly clipped absolute deviation on high dimensions, J. Am. Stat. Assoc, vol.103, issue.484, pp.1665-1673, 2008.

S. Klamt, U. Haus, and F. Theis, Hypergraphs and cellular networks, PLoS Comput. Biol, vol.5, issue.5, p.1000385, 2009.

I. S. Kohane, A. T. Kho, and A. J. Butte, Microarrays for an Integrative Genomics, 2003.

P. Kohli, L. Ladick´yladick´y, T. , and P. H. , Robust higher order potentials for enforcing label consistency, Int. J. Comput. Vis, vol.82, issue.3, pp.302-324, 2009.

T. Kohonen, Self-Organizing Maps, 2000.

V. Kolmogorov and R. Zabih, What energy functions can be minimized via graph cuts?, IEEE Trans. Pattern Anal. Mach. Intell, vol.26, issue.2, pp.147-159, 2004.

, Bibliography 227

N. Komodakis and J. Pesquet, Playing with duality: An overview of recent primal-dual approaches for solving large-scale optimization problems, IEEE Signal Process. Mag, vol.32, issue.6, pp.31-54, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01010437

N. Komodakis, N. Paragios, and G. Tziritas, MRF energy minimization and beyond via dual decomposition, IEEE Trans. Pattern Anal. Mach. Intell, vol.33, issue.3, pp.531-552, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00856311

R. Küffner, T. Petri, P. Tavakkolkhah, L. Windhager, and R. Zimmer, Inferring gene regulatory networks by ANOVA, Bioinformatics, vol.28, issue.10, pp.1376-1382, 2012.

Z. Kurt, N. Aydin, and G. Altay, A comprehensive comparison of association estimators for gene network inference algorithms, Bioinformatics, vol.30, issue.15, pp.2142-2149, 2014.

P. Langfelder and S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, vol.9, issue.1, p.559, 2008.

H. Lantéri and C. Theys, Restoration of astrophysical images-the case of Poisson data with additive Gaussian noise, EURASIP J. Adv. Signal Process, issue.15, pp.2500-2513, 2005.

S. L. Lauritzen, Graphical Models. Oxford Statistical Science Series, 1996.

J. D. Lawson and Y. Lim, The geometric mean, matrices, metrics, and more, Amer. Math. Monthly, vol.108, issue.9, pp.797-812, 2001.

W. Lee and K. Yang, A clustering-based approach for inferring recurrent neural networks as gene regulatory networks, Neurocomputing, vol.71, issue.4-6, pp.600-610, 2008.

N. Leng, J. A. Dawson, J. A. Thomson, V. Ruotti, A. I. Rissman et al., EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments, Bioinformatics, vol.29, issue.8, pp.1035-1043, 2013.

C. Lévy-leduc, M. Delattre, T. Mary-huard, R. , and S. , Two-dimensional segmentation for analyzing HiC data, Bioinformatics, 2014.

H. Li and J. Gui, Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks, Biostatistics, vol.7, issue.2, pp.302-317, 2005.

K. Liang and S. Kele¸skele¸s, Normalization of ChIP-seq data with control, BMC Bioinformatics, vol.13, issue.1, p.199, 2012.

K. Liang and X. Wang, Gene regulatory network reconstruction using conditional mutual information, EURASIP J. Bioinformatics Syst. Biol, pp.1-14, 2008.

S. Liang, S. Fuhrman, and R. Somogyi, REVEAL, a general reverse engineering algorithm for inference of genetic network architectures, In Pac. Symp. Biocomput, vol.3, pp.18-29, 1998.

C. L. Likas and N. P. Galatsanos, A variational approach for Bayesian blind image deconvolution, IEEE Trans. Signal Process, vol.52, issue.8, pp.2222-2233, 2004.

W. K. Lim, K. Wang, C. Lefebvre, and A. Califano, Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks, Bioinformatics, vol.23, issue.13, pp.282-288, 2007.

Y. Lin, K. Golovnina, Z. Chen, H. N. Lee, Y. L. Negron et al., Comparison of normalization and differential expression analyses using RNA-seq data from 726 individual Drosophila melanogaster, BMC Genom, p.17, 2016.

A. Lindöf and B. Olsson, Genetic network inference: the effects of preprocessing, BioSystems, vol.72, issue.3, pp.229-239, 2003.

L. Liu, F. Wu, and W. Zhang, A group LASSO-based method for robustly inferring gene regulatory networks from multiple time-course datasets, BMC Syst. Biol, vol.8, p.1, 2014.

Z. Liu, Reverse engineering of genome-wide gene regulatory networks from gene expression data, Curr. Genom, vol.16, pp.3-22, 2015.

I. Lönnstedt and T. Speed, Replicated microarray data, Statist. Sinica, vol.12, issue.1, pp.31-46, 2002.

T. Lu, H. Liang, H. Li, and H. Wu, High-dimensional ODEs coupled with mixed-effects modeling techniques for dynamic gene regulatory network identification, J. Am. Stat. Assoc, vol.106, issue.496, pp.1242-1258, 2011.

W. Luo, K. D. Hankenson, and P. J. Woolf, Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information, BMC Bioinformatics, vol.9, issue.1, p.467, 2008.

Z. Q. Luo and P. Tseng, On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl, vol.72, issue.1, pp.7-35, 1992.

F. R. Macaulay, The Whittaker-Henderson method of graduation, The Smoothing of Time Series, pp.89-99, 1931.

A. R. Mach-aigner, M. E. Pucher, M. G. Steiger, G. E. Bauer, S. J. Preis et al., Transcriptional regulation of xyr1, encoding the main regulator of the xylanolytic and cellulolytic enzyme system in Hypocrea jecorina, Appl. Environ. Microbiol, vol.74, issue.21, pp.6554-6562, 2008.

K. D. Macisaac, T. Wang, D. B. Gordon, D. K. Gifford, G. D. Stormo et al., An improved map of conserved regulatory sites for Saccharomyces cerevisiae, BMC Bioinformatics, vol.7, issue.1, p.113, 2006.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proc. Fifth Berkeley Symp, vol.1, pp.281-297, 1967.

B. D. Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, An iterative maximum-likelihood polychromatic algorithm for CT, IEEE Trans. Med. Imag, vol.20, issue.10, pp.999-1008, 2001.

D. Marbach, T. Schaffter, C. Mattiussi, and D. Floreano, Generating realistic in silico gene networks for performance assessment of reverse engineering methods, J. Comput. Biol, vol.16, issue.2, pp.229-239, 2009.

D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano et al., Revealing strengths and weaknesses of methods for gene network inference, Proc. Nat. Acad. Sci. U.S.A, vol.107, issue.14, pp.6286-6291, 2010.

D. Marbach, J. C. Costello, R. Küffner, N. M. Vega, R. J. Prill et al., Wisdom of crowds for robust gene network inference, Nat. Meth, vol.9, issue.8, pp.796-804, 2012.

A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky et al., ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinformatics, vol.7, p.7, 2006.

E. Marinari and R. Marra, Cluster algorithms for the generalized 3d, 3q Potts model, Nucl. Phys. B, vol.342, issue.3, pp.737-752, 1990.

Y. Marnissi, E. Chouzenoux, J. Pesquet, and A. Benazza-benyahia, An auxiliary variable method for Langevin based MCMC algorithms, Proc. IEEE Workshop Stat. Signal Process, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01386560

A. Mathelier, X. Zhao, A. W. Zhang, F. Parcy, R. Worsley-hunt et al., JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles, Nucleic Acids Res, vol.42, issue.D1, pp.142-147, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00943558

C. A. Mcgrory, D. M. Titterington, R. Reeves, and A. N. Pettitt, Variational Bayes for estimating the parameters of a hidden Potts model, Stat. Comput, vol.19, issue.3, pp.329-340, 2009.

G. J. Bibliography-mclachlan and T. Krishnan, The EM Algorithm and Extensions, 2008.

M. Meil?-a, Comparing clusterings-an information based distance, J. Multivariate Anal, vol.98, issue.5, pp.873-895, 2007.

M. Meil?-a, Comparing clusterings by the variation of information, Learning Theory and Kernel Machines, vol.2777, pp.173-187, 2003.

N. Meinshausen and P. Bühlmann, High-dimensional graphs and variable selection with the lasso, Ann. Statist, vol.34, issue.3, pp.1436-1462, 2006.

N. Meinshausen and P. Bühlmann, Stability selection, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.72, issue.4, pp.417-473, 2010.

R. Merris, Graph Theory. Series in Discrete Mathematics and Optimization, 2000.

F. Meyer, Minimum spanning forests for morphological segmentation, Mathematical Morphology and Its Applications to Image Processing, pp.77-84, 1994.

P. E. Meyer, K. Kontos, F. Lafitte, and G. Bontempi, Information-theoretic inference of large transcriptional regulatory networks, EURASIP J. Bioinformatics Syst. Biol, pp.1-9, 2007.

P. E. Meyer, F. Lafitte, and G. Bontempi, minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information, Bioinformatics, vol.9, p.461, 2008.

S. Min, B. Lee, and S. Yoon, Deep learning in bioinformatics, Brief. Bioinform, p.68, 2016.

R. Molina, On the hierarchical Bayesian approach to image restoration: applications to astronomical images, IEEE Trans. Pattern Anal. Mach. Intell, vol.16, issue.11, pp.1122-1128, 1994.

R. Molina, A. K. Katsaggelos, and J. Mateos, Bayesian and regularization methods for hyperparameter estimation in image restoration, IEEE Trans. Image Process, vol.8, issue.2, pp.231-246, 1999.

B. S. Montenecourt and D. E. Eveleigh, Preparation of mutants of Trichoderma reesei with enhanced cellulase production, Appl. Environ. Microbiol, vol.34, issue.6, pp.777-782, 1977.

F. Mordelet and J. Vert, SIRENE: supervised inference of regulatory networks, Bioinformatics, vol.24, issue.16, pp.76-82, 2008.
DOI : 10.1093/bioinformatics/btn273
URL : https://hal.archives-ouvertes.fr/hal-00259119

, Bibliography 231

J. J. Moreau, Proximité et dualité dans un espace hilbertien, vol.93, pp.273-299, 1965.
DOI : 10.24033/bsmf.1625
URL : http://www.numdam.org/article/BSMF_1965__93__273_0.pdf

A. Mortazavi, B. Williams, K. Mccue, L. Schaeffer, and B. Wold, Mapping and quantifying mammalian transcriptomes by RNA-Seq, Nat. Meth, vol.5, issue.7, pp.621-628, 2008.
DOI : 10.1038/nmeth.1226

T. Nakari-setälä, M. Paloheimo, J. Kallio, J. Vehmaanperä, M. Penttilä et al., Genetic modification of carbon catabolite repression in Trichoderma reesei for improved protein production, Appl. Environ. Microbiol, vol.75, issue.14, pp.4853-4860, 2009.

J. A. Nelder and R. Mead, A simplex method for function minimization, Comput. J, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

M. E. Newman, Communities, modules and large-scale structure in networks, Nat. Phys, vol.8, issue.1, pp.25-31, 2012.
DOI : 10.1038/nphys2162

M. A. Newton, C. M. Kendziorski, C. S. Richmond, F. R. Blattner, and K. W. Tsui, On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data, J. Comput. Biol, vol.8, issue.1, pp.37-52, 2001.

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Proc. Ann. Conf. Neur. Inform. Proc. Syst, pp.849-856, 2001.

X. Ning, I. W. Selesnick, and L. Duval, Chromatogram baseline estimation and denoising using sparsity (BEADS), Chemometr. Intell. Lab. Syst, vol.139, pp.156-167, 2014.
DOI : 10.1016/j.chemolab.2014.09.014
URL : https://hal.archives-ouvertes.fr/hal-01330608

G. Obozinski, L. Jacob, and J. Vert, Group lasso with overlaps: the latent group lasso approach, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00628498

D. O'connor and L. Vandenberghe, Total variation image deblurring with spacevarying kernel via Douglas-Rachford splitting, Comput. Optim. Appl, 2017.

S. Okawa, V. E. Angarica, I. Lemischka, K. Moore, and A. Sol, A differential network analysis approach for lineage specifier prediction in stem cell subpopulations, NPJ Syst. Biol. Appl, vol.1, p.15012, 2015.

J. Ollion, J. Cochennec, F. Loll, C. Escudé, and T. Boudier, TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization, Bioinformatics, vol.29, issue.14, pp.1840-1841, 2013.
DOI : 10.1093/bioinformatics/btt276
URL : https://academic.oup.com/bioinformatics/article-pdf/29/14/1840/16915549/btt276.pdf

N. Omranian, J. M. Eloundou-mbebi, B. Mueller-roeber, and Z. Nikoloski, , 2016.

, Gene regulatory network inference using fused LASSO on multiple data sets, Sci. Rep, vol.6, p.20533

S. Orchard, M. Ammari, B. Aranda, L. Breuza, L. Briganti et al., , p.232

M. Bibliography, E. Galeota, U. Hinz, M. Iannuccelli, S. Jagannathan et al., The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases, Nucleic Acids Res, vol.42, issue.D1, pp.358-363, 2013.

J. M. Ortega and W. C. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables, 1970.

M. R. Osborne, B. Presnell, and B. A. Turlach, On the LASSO and its dual, DEPRECATED, USE j-comp-graph-stat INSTEAD), vol.9, pp.319-337, 2000.
DOI : 10.2307/1390657

A. Oshlack, M. D. Robinson, and M. D. Young, From RNA-seq reads to differential expression results, Genome Biol, vol.11, issue.12, pp.1-10, 2010.

R. Pal, A. Datta, M. L. Bittner, and E. R. Dougherty, Intervention in contextsensitive probabilistic boolean networks, Bioinformatics, vol.21, issue.7, pp.1211-1218, 2004.

J. A. Palmer, D. P. Wipf, K. Kreutz-delgado, and B. D. Rao, Variational EM algorithms for non-Gaussian latent variable models, Proc. Ann. Conf. Neur. Inform. Proc. Syst, pp.1059-1066, 2005.

J. R. Parikh, Y. Xia, and J. A. Marto, Multi-edge gene set networks reveal novel insights into global relationships between biological themes, PLoS One, vol.7, issue.9, p.45211, 2012.

N. Parikh and S. Boyd, Proximal algorithms, Found. Trends Optim, vol.1, issue.3, pp.123-231, 2013.

G. Parisi, Statistical Field Theory, 1998.

T. Park, S. Yi, S. Kang, S. Y. Lee, Y. Lee et al., Evaluation of normalization methods for microarray data, BMC Bioinformatics, vol.4, issue.1, pp.1-13, 2003.

G. Paul, J. Cardinale, and I. F. Sbalzarini, Coupling image restoration and segmentation: A generalized linear model/Bregman perspective, Int. J. Comput. Vis, vol.104, issue.1, pp.69-93, 2013.

D. Pe'er, A. Regev, G. Elidan, and N. Friedman, Inferring subnetworks from perturbed expression profiles, Bioinformatics, vol.17, pp.215-224, 2001.

D. Pelleg and A. Moore, X-means: Extending K-means with efficient estimation of the number of clusters, Proc. Int. Conf. Mach. Learn, pp.727-734, 2000.

M. Pereyra and S. Mclaughlin, Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation, IEEE Trans. Image Process, 2017.

M. Pereyra, N. Dobigeon, H. Batatia, and J. Tourneret, Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized rayleigh mixture model, IEEE Trans. Med. Imag, vol.31, issue.8, pp.1509-1520, 2012.

M. Pereyra, N. Dobigeon, H. Batatia, and J. Tourneret, Estimating the granularity coefficient of a Potts-Markov random field within a Markov chain Monte Carlo algorithm, IEEE Trans. Image Process, vol.22, issue.6, pp.2385-2397, 2013.

B. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet et al., Gene networks inference using dynamic Bayesian networks, Bioinformatics, vol.19, issue.2, pp.138-148, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01176902

G. Peyré, A review of adaptive image representations, IEEE J. Sel. Topics Signal Process, vol.5, issue.5, pp.896-911, 2011.

M. Q. Pham, L. Duval, C. Chaux, and J. Pesquet, A primal-dual proximal algorithm for sparse template-based adaptive filtering: Application to seismic multiple removal, IEEE Trans. Signal Process, vol.62, issue.16, pp.4256-4269, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00914628

A. Pirayre, C. Couprie, F. Bidard, L. Duval, and J. Pesquet, BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference, BMC Bioinformatics, vol.16, issue.1, p.369, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01330611

A. Pirayre, C. Couprie, L. Duval, and J. Pesquet, Fast convex optimization for connectivity enforcement in gene regulatory network inference, Proc. Int. Conf. Acoust. Speech Signal Process, pp.1002-1006, 2015.

A. Pirayre, C. Couprie, L. Duval, and J. Pesquet, Graph inference enhancement with clustering: Application to gene regulatory network reconstruction, Proc. Eur. Sig. Image Proc. Conf, pp.2406-2410, 2015.

A. Pirayre, Y. Zheng, J. Pesquet, and L. Duval, HOGMep: variational Bayes and higher-order graphical models applied to joint image recovery and segmentation, Proc. Int. Conf. Image Process, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01862840

A. Pirayre, C. Couprie, L. Duval, and J. Pesquet, BRANE Clust: clusterassisted gene regulatory network inference refinement, IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol.15, issue.3, pp.850-860, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01330638

A. Pirayre, D. Ivanoff, L. Duval, C. Blugeon, C. Firmo et al., Growing Trichoderma reseei on a mix of carbon sources reveals links between development and cellulase production, 2018.

H. Pirim, B. Ek¸sio?ek¸sio?-glu, A. D. Perkins, and C. ¸. Yüceer, Clustering of high throughput gene expression data, Comput. Oper. Res, vol.39, pp.3046-3061, 2012.

A. Bibliography-pi?urica, V. Zlokolica, and W. Philips, Noise reduction in video sequences using wavelet-domain and temporal filtering, Proc. SPIE, 2004.

D. Poggi-parodi, F. Bidard, A. Pirayre, T. Portnoy, C. Blugeon et al., Kinetic transcriptome analysis reveals an essentially intact induction system in a cellulase hyper-producer Trichoderma reesei strain, Biotechnol. Biofuels, vol.7, issue.1, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01112360

A. Polynikis, S. Hogan, and M. Bernardo, Comparing different ODE modelling approaches for gene regulatory networks, J. Theor. Biol, vol.261, issue.4, pp.511-530, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00554639

J. Portilla, V. Strela, M. Wainwright, and E. Simoncelli, Image denoising using scale mixtures of gaussians in the wavelet domain, IEEE Trans. Image Process, vol.12, issue.11, pp.1338-1351, 2003.

T. Portnoy, Analyse du transcriptome de Trichoderma reesei pour l'amélioration de la production de cellulases, 2011.

T. Portnoy, A. Margeot, R. Linke, L. Atanasova, E. Fekete et al., The CRE1 carbon catabolite repressor of the fungus Trichoderma reesei : a master regulator of carbon assimilation, BMC Genom, vol.12, p.269, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00663944

G. Prelich, Gene overexpression: Uses, mechanisms, and interpretation, Genetics, vol.190, issue.3, pp.841-854, 2012.

R. J. Prill, D. Marbach, J. Saez-rodriguez, P. K. Sorger, L. G. Alexopoulos et al., Towards a rigorous assessment of systems biology models: the DREAM3 challenges, PLoS One, vol.5, issue.2, p.9202, 2010.

N. Pustelnik, A. Benazza-benhayia, Y. Zheng, and J. Pesquet, Waveletbased image deconvolution and reconstruction, Wiley Encyclopedia of Electrical and Electronics Engineering, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01164833

Y. Qin, L. Bao, M. Gao, M. Chen, Y. Lei et al., Penicillium decumbens BrlA extensively regulates secondary metabolism and functionally associates with the expression of cellulase genes, Appl. Microbiol. Biotechnol, vol.97, issue.24, pp.10453-10467, 2013.

J. Quackenbush, Microarray data normalization and transformation, Nat. Genet, vol.32, pp.496-501, 2002.

D. Quang and X. Xie, EXTREME: an online EM algorithm for motif discovery, Bioinformatics, vol.30, issue.12, pp.1667-1673, 2014.

, Bibliography 235

W. M. Rand, Objective criteria for the evaluation of clustering methods, J. Am. Stat. Assoc, vol.66, issue.336, pp.846-850, 1971.

F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, and J. Vert, Classification of microarray data using gene networks, BMC Bioinformatics, vol.8, issue.1, p.35, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00433577

A. Repetti, M. Q. Pham, L. Duval, E. Chouzenoux, and J. Pesquet, Euclid in a taxicab: Sparse blind deconvolution with smoothed ? 1 ? 2 regularization, IEEE Signal Process. Lett, vol.22, issue.5, pp.539-543, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01328398

D. N. Reshef, Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. Mcvean et al., Detecting novel associations in large data sets, Science, vol.334, issue.6062, pp.1518-1524, 2011.

H. W. Ressom, Y. Zhang, J. Xuan, Y. Wang, C. et al., Inference of gene regulatory networks from time course gene expression data using neural networks and swarm intelligence, Proc. IEEE Symp, 2006.

N. Reymond, Bioinformatique des pucesàpucesà ADN et applicationàapplicationà l'analyse du transcriptome de Buchnera aphidicola, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00391275

C. P. Robert and G. Casella, , 2004.

, Monte Carlo Statistical Methods

M. D. Robinson and A. Oshlack, A scaling normalization method for differential expression analysis of RNA-seq data, Genome Biol, vol.11, issue.3, p.25, 2010.

M. D. Robinson and G. K. Smyth, Moderated statistical tests for assessing differences in tag abundance, Bioinformatics, vol.23, issue.21, pp.2881-2887, 2007.

M. D. Robinson and G. K. Smyth, Small-sample estimation of negative binomial dispersion, with applications to SAGE data, Biostatistics, vol.9, issue.2, pp.321-332, 2008.

M. D. Robinson, D. J. Mccarthy, and G. K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, vol.26, issue.1, pp.139-140, 2009.

T. Rodet, F. Orieux, J. Giovannelli, A. , and A. , Data inversion for over-resolved spectral imaging in astronomy, IEEE J. Sel. Topics Signal Process, vol.2, issue.5, pp.802-811, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00411775

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math, vol.20, issue.0, pp.53-65, 1987.

S. Roy, S. Lagree, Z. Hou, J. A. Thomson, R. Stewart et al., Integrated module and gene-specific regulatory inference implicates upstream signaling networks, PLoS Comput. Biol, vol.9, issue.10, p.1003252, 2013.

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D, vol.60, issue.1-4, pp.259-268, 1992.

J. Ruyssinck, V. A. Huynh-thu, P. Geurts, T. Dhaene, P. Demeester et al., NIMEFI: Gene regulatory network inference using multiple ensemble feature importance algorithms, PLoS One, vol.9, issue.3, p.92709, 2014.

A. Saadatpour and R. Albert, Boolean modeling of biological regulatory networks: A methodology tutorial, Methods, vol.62, issue.1, pp.3-12, 2013.

A. Saloheimo, Isolation of the ace1 gene encoding a Cys2-His2 transcription factor involved in regulation of activity of the cellulase promoter cbh1 of Trichoderma reesei, J. Biol. Chem, vol.275, issue.8, pp.5817-5825, 2000.

L. Salwinski, C. S. Miller, A. J. Smith, F. K. Pettit, J. U. Bowie et al., The database of interacting proteins: 2004 update, Nucleic Acids Res, vol.32, issue.90001, pp.449-451, 2004.

A. Sandelin, W. Alkema, P. Engström, W. W. Wasserman, and B. Lenhard, JASPAR: an open-access database for eukaryotic transcription factor binding profiles, Nucleic Acids Res, vol.32, issue.90001, pp.91-94, 2004.

J. Schäfer and K. Strimmer, A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics, Statist. Appl. Genet. Mol, vol.4, issue.1, 2005.

T. Schaffter, D. Marbach, and D. Floreano, GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods, Bioinformatics, vol.27, issue.16, pp.2263-2270, 2011.

A. Scherer, Batch Effects and Noise in Microarray Experiments: Sources and Solutions, 2009.

S. L. Schreiber, Small molecules: the missing link in the central dogma, Nat. Chem. Biol, vol.1, issue.2, pp.64-66, 2005.

G. Schwarz, Estimating the dimension of a model, Ann. Statist, vol.6, issue.2, pp.461-464, 1978.

E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein et al., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nat. Genet, vol.34, issue.2, pp.166-176, 2003.

B. Seiboth, R. A. Karimi, P. A. Phatale, R. Linke, L. Hartl et al., The putative protein methyltransferase LAE1 controls cellulase gene expression in Trichoderma reesei, Mol. Microbiol, vol.84, issue.6, pp.1150-1164, 2012.

, Bibliography 237

V. Seidl, C. Gamauf, I. S. Druzhinina, B. Seiboth, L. Hartl et al., , 2008.

, The Hypocrea jecorina (Trichoderma reesei ) hypercellulolytic mutant RUT c30 lacks a 85 kb (29 gene-encoding) region of the wild-type genome, BMC Genom, vol.9, issue.1, p.327

N. Servant, N. Varoquaux, B. R. Lajoie, E. Viara, C. Chen et al., HiC-pro: an optimized and flexible pipeline for Hi-C data processing, Genome Biol, vol.16, issue.1, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01246671

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.8, pp.888-905, 2000.

T. Shimamura, S. Imoto, R. Yamaguchi, M. Nagasaki, and S. Miyano, Inferring dynamic gene networks under varying conditions for transcriptomic network comparison, Bioinformatics, vol.26, issue.8, pp.1064-1072, 2010.

I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks, Bioinformatics, vol.18, issue.2, pp.261-274, 2002.

D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Process. Mag, vol.30, issue.3, pp.83-98, 2013.

K. I. Siddiqui, A. O. Hero, and M. M. Siddiqui, Mathematical morphology applied to spot segmentation and quantification of gene microarray images, Proc. Asilomar Conf. Signal Syst. Comput, pp.926-930, 2002.

C. Siegenthaler and R. Gunawan, Assessment of network inference methods: How to cope with an underdetermined problem, PLoS One, vol.9, issue.3, p.90481, 2014.

A. Silvescu and V. Honavar, Temporal boolean network models of genetic networks and their inference from gene expression time series, Complex systems, vol.13, 2001.

N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, A sparse-group lasso, DEPRECATED, USE j-comp-graph-stat INSTEAD), vol.22, pp.231-245, 2013.

D. Singaraju, L. Grady, A. K. Sinop, and R. Vidal, Continuous valued MRFs for image segmentation, Markov Random Fields for Vision and Image Processing, pp.127-142, 2011.

N. Singh and M. Vidyasagar, bLARS: An algorithm to infer gene regulatory networks, IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol.13, issue.2, pp.301-314, 2016.

V. Smídl and A. Quinn, The Variational Bayes Method in Signal Processing, 2006.

G. K. Smyth, Linear models and empirical Bayes methods for assessing differential expression in microarray experiments, Stat. Appl. Genet. Mol. Biol, vol.3, issue.1, pp.1-25, 2004.

G. K. Smyth, limma: linear models for microarray data, Bioinformatics and Computational Biology Solutions using R and Bioconductor, pp.397-420, 2005.

G. K. Smyth and T. Speed, Normalization of cDNA microarray data, Methods, vol.31, issue.4, pp.265-273, 2003.

J. Sodjo, A. Giremus, F. Caron, J. Giovannelli, D. et al., Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approach, Proc. IEEE Workshop Stat. Signal Process, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01500506

L. A. Soinov, M. A. Krestyaninova, and A. Brazma, Towards reconstruction of gene networks from expression data by supervised learning, Genome Biol, vol.4, issue.1, p.6, 2003.

C. Soneson and M. Delorenzi, A comparison of methods for differential expression analysis of RNA-seq data, BMC Bioinformatics, vol.14, issue.1, p.91, 2013.

M. Sonka and J. M. Fitzpatrick, Handbook of Medical Imaging, Medical Image Processing and Analysis, vol.2, 2000.

N. Soranzo, G. Bianconi, A. , and C. , Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data, Bioinformatics, vol.23, issue.13, pp.1640-1647, 2007.

P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders et al., Comprehensive identification of cell cycleregulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Mol. Biol. Cell, vol.9, issue.12, pp.3273-3297, 1998.

F. W. Stearns, One hundred years of pleiotropy: A retrospective, Genetics, vol.186, issue.3, pp.767-773, 2010.

H. Steinhaus, Sur la division des corps matériels en parties, Bull. Acad. Polon. Sci., Cl. III, vol.IV, issue.12, pp.801-804, 1956.

J. Strauss, R. L. Mach, S. Zeilinger, G. Hartler, G. Stöffler et al., Crel, the carbon catabolite repressor protein from Trichoderma reesei, FEBS Lett, vol.376, issue.1-2, pp.103-107, 1995.

A. R. Stricker, K. Grosstessner-hain, E. Würleitner, and R. L. Mach, Xyr1 (xylanase regulator 1) regulates both the hydrolytic enzyme system and D-xylose metabolism in Hypocrea jecorina, Eukaryot. Cell, vol.5, issue.12, pp.2128-2137, 2006.

, Bibliography 239

A. R. Stricker, R. L. Mach, and L. H. De-graaff, Regulation of transcription of cellulases-and hemicellulases-encoding genes in Aspergillus niger and Hypocrea jecorina (Trichoderma reesei ), Appl. Microbiol. Biotechnol, vol.78, issue.2, pp.211-220, 2008.

J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, A gene-coexpression network for global discovery of conserved genetic modules, Science, vol.302, issue.5643, pp.249-255, 2003.

M. Sugiyama, C. Azencott, D. Grimm, Y. Kawahara, and K. M. Borgwardt, , 2014.

, Multi-task feature selection on multiple networks via maximum flows, Proc. SIAM Int. Conf. Data Mining, pp.199-207

Y. Tamada, S. Kim, H. Bannai, S. Imoto, K. Tashiro et al., Estimating gene networks from gene expression data by combining bayesian network model with promoter element detection, Bioinformatics, vol.19, issue.2, pp.227-236, 2003.

Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, Segmentation and classification of hyperspectral images using watershed transformation, Pattern Recogn, vol.43, issue.7, pp.2367-2379, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00578860

J. N. Taroni and C. S. Greene, Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously, 2017.

M. Thomas-cholier, O. Sand, J. Turatsinze, R. Janky, M. Defrance et al., RSAT: regulatory sequence analysis tools, Nucleic Acids Res, vol.36, pp.119-127, 2008.

R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.58, issue.1, pp.267-288, 1996.

R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, Sparsity and smoothness via the fused lasso, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.67, issue.1, pp.91-108, 2005.

A. N. Tikhonov, On the solution of ill-posed problems and the method of regularization, Dokl. Akad. Nauk SSSR, vol.151, pp.501-504, 1963.

H. Toh and K. Horimoto, Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling, Bioinformatics, vol.18, issue.2, pp.287-297, 2002.

V. G. Tusher, R. Tibshirani, C. , and G. , Significance analysis of microarrays applied to the ionizing radiation response, Proc. Nat. Acad. Sci. U.S.A, vol.98, issue.9, pp.5116-5121, 2001.

M. Unger, T. Pock, W. Trobin, D. Cremers, and H. Bischof, TVSeg -interactive total variation based image segmentation, Proc. Brit. Machine Vis. Conf., pages, 2008.

D. Van-de-ville, R. Demesmaeker, and M. G. Preti, When Slepian meets Fiedler: Putting a focus on the graph spectrum, IEEE Signal Process. Lett, 2017.

E. P. Van-someren, B. L. Vaes, W. T. Steegenga, A. M. Sijbers, K. J. Dechering et al., Least absolute regression network analysis of the murine osteoblast differentiation network, Bioinformatics, vol.22, issue.4, pp.477-484, 2005.

J. Vert, Les applications industrielles de la bio-informatique, Annales des Mines -Réalités industrielles, pp.17-23, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00796732

M. Vignes, J. Vandel, D. Allouche, N. Ramadan-alban, C. Cierco-ayrolles et al., Gene regulatory network reconstruction using Bayesian networks, the Dantzig selector, the lasso and their meta-analysis, PLoS One, vol.6, issue.12, p.29165, 2011.

N. X. Vinh, M. Chetty, R. Coppel, and P. P. Wangikar, Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network, BMC Bioinformatics, vol.13, issue.1, p.131, 2012.

C. Von-mering, L. J. Jensen, B. Snel, S. D. Hooper, M. Krupp et al., STRING: known and predicted proteinprotein associations, integrated and transferred across organisms, Nucleic Acids Res, vol.33, pp.433-437, 2005.

M. Wahde and J. Hertz, Coarse-grained reverse engineering of genetic regulatory networks, BioSystems, vol.55, issue.1-3, pp.129-136, 2000.

M. Wand, J. T. Ormerod, S. A. Padoan, and R. Fruhwirth, Variational Bayes for elaborate distributions, 2010.

J. Wang, J. Z. Ma, and M. D. Li, Normalization of cDNA microarray data using wavelet regressions, Comb. Chem. High Throughput Screen, vol.7, issue.8, pp.783-791, 2004.

M. Wang, N. Jiang, T. Jia, L. Leach, J. Cockram et al., Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars, Theor. Appl. Genet, vol.124, issue.2, pp.233-246, 2012.

R. Wang, A. Saadatpour, A. , and R. , Boolean modeling in systems biology: an overview of methodology and applications, Phys. Biol, vol.9, issue.5, p.55001, 2012.

Y. Wang, T. Joshi, X. Zhang, D. Xu, C. et al., Inferring gene regulatory networks from multiple microarray datasets, Bioinformatics, vol.22, issue.19, pp.2413-2420, 2006.

R. L. Wasserstein and N. A. Lazar, The ASA's statement on p-values: context, process, and purpose, Am. Stat, vol.70, issue.2, pp.129-133, 2016.

, Bibliography

D. C. Weaver, C. T. Workman, and G. D. Stormo, Modeling regulatory networks with weight matrices, In Pac. Symp. Biocomput, 1999.

A. V. Werhli and D. Husmeier, Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge, Stat. Appl. Genet. Mol. Biol, vol.6, issue.1, 2007.

E. T. Whittaker, On a new method of graduation, Proceedings of the Edinburgh Mathematical Society, vol.41, pp.63-75, 1922.

J. Whittaker, Graphical models in applied multivariate statistics. Probability and mathematical statistics, 1990.

A. Wille, P. Zimmermann, E. Vranoá, A. Füholz, O. Laule et al., Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana, Genome Biol, vol.5, issue.11, p.92, 2004.

C. Workman, L. J. Jensen, H. Jarmer, R. Berka, L. Gautier et al., A new non-linear normalization method for reducing variability in DNA microarray experiments, Genome Biol, vol.3, issue.9, 2002.

S. Wu, Z. Liu, X. Qiu, and H. Wu, Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations, PLoS One, vol.9, issue.5, p.95276, 2014.

R. Xu, G. K. Venayagamoorthy, and D. C. Wunsch, Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization, Neural Netw, vol.20, issue.8, pp.917-927, 2007.

I. V. Yang, E. Chen, J. P. Hasseman, W. Liang, B. C. Frank et al., Within the fold: assessing differential expression measures and reproducibility in microarray assays, 2002.

, Genome Biol, vol.3, issue.11

Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed, Normalization for cDNA microarry data, Proc. SPIE, vol.4266, pp.141-152, 2001.

Y. H. Yang, S. Dudoit, P. Luu, D. M. Lin, V. Peng et al., Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation, Nucleic Acids Res, vol.30, issue.4, p.15, 2002.

D. Yanming, D. W. Schafer, J. S. Cumbie, C. , and J. H. , The NBP negative binomial model for assessing differential gene expression from RNA-Seq, Stat. Appl. Genet. Mol. Biol, vol.10, issue.1, pp.1-28, 2011.

M. K. Yeung, J. Tegner, and J. J. Collins, Reverse engineering gene networks using singular value decomposition and robust regression, Proc. Nat. Acad. Sci. U.S.A, vol.99, issue.9, pp.6163-6168, 2002.

W. Young, A. E. Raftery, and K. Yeung, Fast Bayesian inference for gene regulatory networks using ScanBMA, BMC Syst. Biol, vol.8, issue.1, p.47, 2014.

F. Y. Yu, The Potts model, Rev. Mod. Phys, vol.54, issue.1, 1982.

J. Yu, V. A. Smith, P. P. Wang, A. J. Hartemink, J. et al., Advances to Bayesian network inference for generating causal networks from observational biological data, Bioinformatics, vol.20, issue.18, pp.3594-3603, 2004.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.68, issue.1, pp.49-67, 2006.

A. Zhang, C. Tang, and D. Jiang, Cluster analysis for gene expression data: A survey, IEEE Trans. Knowl. Data Eng, vol.16, issue.11, pp.1370-1386, 2004.

X. Zhang, K. Liu, Z. Liu, B. Duval, J. Richer et al., NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference, Bioinformatics, vol.29, issue.1, pp.106-113, 2013.

N. Zhao, A. Basarab, D. Kouame, and J. Tourneret, Joint segmentation and deconvolution of ultrasound images using a hierarchical bayesian model based on generalized gaussian priors, IEEE Trans. Image Process, vol.25, issue.8, pp.3736-3750, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01374064

S. Zhao, Y. Yan, Q. He, L. Yang, X. Yin et al., Comparative genomic, transcriptomic and secretomic profiling of Penicillium oxalicum HP7-1 and its cellulase and xylanase hyperproducing mutant EU2106, and identification of two novel regulatory genes of cellulase and xylanase gene expression, Biotechnol. Biofuels, issue.1, p.9, 2016.

W. Zhao, E. Serpedin, and E. R. Dougherty, Inferring connectivity of genetic regulatory networks using information-theoretic criteria, IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol.5, issue.2, pp.262-274, 2008.

E. Zheleva, L. Getoor, and S. Sarawagi, Higher-order graphical models for classification in social and affiliation networks, NIPS Workshop on Networks Across Disciplines: Theory and Applications, 2010.

Y. Zheng, A. Fraysse, and T. Rodet, Efficient variational Bayesian approximation method based on subspace optimization, IEEE Trans. Image Process, vol.24, issue.2, pp.681-693, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00990003

, Bibliography 243

Y. Zheng, A. Fraysse, and T. Rodet, Wavelet based unsupervised variational Bayesian image reconstruction approach, Proc. Eur. Sig. Image Proc. Conf, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01266464

M. Zibulevsky and B. A. Pearlmutter, Blind source separation by sparse decomposition in a signal dictionary, Neural Comput, vol.13, issue.4, pp.863-882, 2001.

A. Zien, T. Aigner, R. Zimmer, and T. Lengauer, Centralization: a new method for the normalization of gene expression data, Bioinformatics, vol.17, pp.323-331, 2001.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.67, issue.2, pp.301-320, 2005.