@article{ggmgsa2014,
  author = 	 {St\"adler, Nicolas and Mukherjee, Sach},
  title = 	 {Multivariate gene-set testing based on graphical models},
  journal   = {Biostatistics, doi: 10.1093/biostatistics/kxu027},
  year = 	 {2014}
 }

@Book{rue2005,
  author = 	 {Rue, H. and Held, L.},
  title = 	 {Gaussian Markov random fields: theory and applications},
  publisher = 	 {CRC Press},
  year = 	 {2005}
}

@Article{friedman2007sic,
  title={Sparse inverse covariance estimation with the graphical {L}asso},
  author={Friedman, J. and Hastie, T. and Tibshirani, R.},
  journal={Biostatistics},
  year={2008},
  volume={9},
  number={3},
 pages={432-441}
}

@Article{huge2012,
  author = 	 {Tuo Zhao and Han Liu and Kathryn Roeder and John Lafferty and Larry Wasserman},
  title = 	 {The huge Package for High-dimensional Undirected Graph Estimation in {R}},
  journal = 	 {Journal of Machine Learning Research},
  year = 	 {2012},
  OPTkey = 	 {},
  volume = 	 {13},
  OPTnumber = 	 {4},
  pages = 	 {1059--1062}
}

@article{ARACNE,
	title = {{ARACNE}: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context},
	volume = {7},
	copyright = {2006 The Author(s); licensee {BioMed} Central Ltd.},
	issn = {1471-2105},
	shorttitle = {{ARACNE}},
	url = {http://www.biomedcentral.com/1471-2105/7/S1/S7},
	doi = {10.1186/1471-2105-7-S1-S7},
	abstract = {Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present {ARACNE}, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.
{PMID}: 16723010},
	language = {en},
	number = {Suppl 1},
	urldate = {2014-09-23},
	journal = {{BMC} Bioinformatics},
	author = {Margolin, Adam A. and Nemenman, Ilya and Basso, Katia and Wiggins, Chris and Stolovitzky, Gustavo and Favera, Riccardo D. and Califano, Andrea},
	month = mar,
	year = {2006},
	pmid = {16723010},
	pages = {S7},
	file = {Full Text PDF:/local/tmp/Zotero/storage/8RHHHW96/Margolin et al. - 2006 - ARACNE An Algorithm for the Reconstruction of Gen.pdf:application/pdf;Snapshot:/local/tmp/Zotero/storage/I6H6VGWF/S7.html:text/html}
}




@article{EDISON,
	title = {Non-homogeneous dynamic {B}ayesian networks with {B}ayesian regularization for inferring gene regulatory networks with gradually time-varying structure},
	volume = {90},
	issn = {0885-6125, 1573-0565},
	url = {http://link.springer.com/article/10.1007/s10994-012-5311-x},
	doi = {10.1007/s10994-012-5311-x},
	abstract = {The proper functioning of any living cell relies on complex networks of gene regulation. These regulatory interactions are not static but respond to changes in the environment and evolve during the life cycle of an organism. A challenging objective in computational systems biology is to infer these time-varying gene regulatory networks from typically short time series of transcriptional profiles. While homogeneous models, like conventional dynamic Bayesian networks, lack the flexibility to succeed in this task, fully flexible models suffer from inflated inference uncertainty due to the limited amount of available data. In the present paper we explore a semi-flexible model based on a piecewise homogeneous dynamic Bayesian network regularized by gene-specific inter-segment information sharing. We explore different choices of prior distribution and information coupling and evaluate their performance on synthetic data. We apply our method to gene expression time series obtained during the life cycle of Drosophila melanogaster, and compare the predicted segmentation with other state-of-the-art techniques. We conclude our evaluation with an application to synthetic biology, where the objective is to predict an in vivo regulatory network of five genes in Saccharomyces cerevisiae subjected to a changing environment.},
	language = {en},
	number = {2},
	urldate = {2014-09-23},
	journal = {Machine Learning},
	author = {Dondelinger, Frank and L\`{e}bre, Sophie and Husmeier, Dirk},
	month = feb,
	year = {2013},
	keywords = {Artificial Intelligence (incl. Robotics), Computing Methodologies, Control, Robotics, Mechatronics, Dynamic Bayesian networks, Gene expression time series, Hierarchical Bayesian models, Language Translation and Linguistics, Multiple changepoint processes, Reversible jump Markov chain Monte Carlo, Simulation and Modeling, Systems and synthetic biology},
	pages = {191--230},
	file = {Snapshot:/local/tmp/Zotero/storage/9MT6B88K/s10994-012-5311-x.html:text/html}
}


@book{bnlearn,
	title = {Bayesian Networks in R - with Applications in Systems Biology},
	url = {http://www.springer.com/statistics/computational+statistics/book/978-1-4614-6445-7},
	abstract = {Bayesian Networks in R with Applications in Systems Biology introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The ...},
	urldate = {2014-09-23},
	keywords = {Bayesian Networks in R - with Applications in Systems Biology, Statistical Theory and Methods, Statistics and Computing / Statistics Programs},
	file = {Snapshot:/local/tmp/Zotero/storage/E7K2RXAQ/978-1-4614-6445-7.html:text/html}
}


@article{RTN,
	title = {Master regulators of {FGFR}2 signalling and breast cancer risk},
	volume = {4},
	copyright = {© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	url = {http://www.nature.com/ncomms/2013/130917/ncomms3464/full/ncomms3464.html?message-global=remove},
	doi = {10.1038/ncomms3464},
	abstract = {The fibroblast growth factor receptor 2 ({FGFR}2) locus has been consistently identified as a breast cancer risk locus in independent genome-wide association studies. However, the molecular mechanisms underlying {FGFR}2-mediated risk are still unknown. Using model systems we show that {FGFR}2-regulated genes are preferentially linked to breast cancer risk loci in expression quantitative trait loci analysis, supporting the concept that risk genes cluster in pathways. Using a network derived from 2,000 transcriptional profiles we identify {SPDEF}, {ERα}, {FOXA}1, {GATA}3 and {PTTG}1 as master regulators of fibroblast growth factor receptor 2 signalling, and show that {ERα} occupancy responds to fibroblast growth factor receptor 2 signalling. Our results indicate that {ERα}, {FOXA}1 and {GATA}3 contribute to the regulation of breast cancer susceptibility genes, which is consistent with the effects of anti-oestrogen treatment in breast cancer prevention, and suggest that fibroblast growth factor receptor 2 signalling has an important role in mediating breast cancer risk.},
	language = {en},
	urldate = {2014-09-23},
	journal = {Nature Communications},
	author = {Fletcher, Michael N. C. and Castro, Mauro A. A. and Wang, Xin and de Santiago, Ines and O’Reilly, Martin and Chin, Suet-Feung and Rueda, Oscar M. and Caldas, Carlos and Ponder, Bruce A. J. and Markowetz, Florian and Meyer, Kerstin B.},
	month = sep,
	year = {2013},
	keywords = {Bioinformatics, Biological sciences, Cancer, Genetics},
	file = {Full Text PDF:/local/tmp/Zotero/storage/6CRBHJS3/Fletcher et al. - 2013 - Master regulators of FGFR2 signalling and breast c.pdf:application/pdf;Snapshot:/local/tmp/Zotero/storage/Q5FMQ5KK/ncomms3464.html:text/html}
}


@article{tcga_breast,
	title = {Comprehensive molecular portraits of human breast tumours},
	volume = {490},
	copyright = {© 2012 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {0028-0836},
	url = {http://www.nature.com/nature/journal/v490/n7418/full/nature11412.html},
	doi = {10.1038/nature11412},
	abstract = {We analysed primary breast cancers by genomic {DNA} copy number arrays, {DNA} methylation, exome sequencing, messenger {RNA} arrays, {microRNA} sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes ({TP}53, {PIK}3CA and {GATA}3) occurred at {\textgreater}10\% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in {GATA}3, {PIK}3CA and {MAP}3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a {HER}2/phosphorylated {HER}2/{EGFR}/phosphorylated {EGFR} signature within the {HER}2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.},
	language = {en},
	number = {7418},
	urldate = {2013-09-20},
	journal = {Nature},
	author = {Network, The Cancer Genome Atlas},
	month = oct,
	year = {2012},
	keywords = {Cancer, Genetics, Genomics, molecular biology, pancan {II}},
	pages = {61--70},
	file = {Full Text PDF:/local/tmp/Zotero/storage/R5Q8C8HF/Network - 2012 - Comprehensive molecular portraits of human breast .pdf:application/pdf;Snapshot:/local/tmp/Zotero/storage/IH3SVFRV/nature11412.html:text/html}
}


@article{glasso,
	title = {Sparse inverse covariance estimation with the graphical lasso},
	volume = {9},
	issn = {1465-4644, 1468-4357},
	url = {http://biostatistics.oxfordjournals.org/content/9/3/432},
	doi = {10.1093/biostatistics/kxm045},
	abstract = {We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.},
	language = {en},
	number = {3},
	urldate = {2014-09-23},
	journal = {Biostatistics},
	author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
	month = jul,
	year = {2008},
	pmid = {18079126},
	keywords = {Gaussian covariance, Graphical model, L1, Lasso},
	pages = {432--441},
	file = {Full Text PDF:/local/tmp/Zotero/storage/P9EE4TD5/Friedman et al. - 2008 - Sparse inverse covariance estimation with the grap.pdf:application/pdf;Snapshot:/local/tmp/Zotero/storage/8JQZBJCN/432.html:text/html}
}

@article{staedler_mixglasso,
  author = 	 {St\"adler, Nicolas and Mukherjee, Sach},
  title = 	 {Penalized estimation in high-dimensional hidden {M}arkov models with state-specific graphical models},
journal = 	 {Annals of Applied Statistics},
  year = 	 {2013},
  OPTkey = 	 {},
  volume = 	 {7},
  OPTnumber = 	 {},
  pages = 	 {2157--2179}
 }

@article{staedler_diffnet,
  author = 	 {St\"adler, Nicolas and Mukherjee, Sach},
  title = 	 {Two-Sample Testing in High-Dimensional Models},
  journal   = {arXiv.org:1210.4584},
  year = 	 {2013}
 }

@article{BIC,
  title={Estimating the dimension of a model},
  author={Schwarz, Gideon and others},
  journal={The annals of statistics},
  volume={6},
  number={2},
  pages={461--464},
  year={1978},
  publisher={Institute of Mathematical Statistics}
}

@article{MMDL,
  title={Model selection and the principle of minimum description length},
  author={Hansen, Mark H and Yu, Bin},
  journal={Journal of the American Statistical Association},
  volume={96},
  number={454},
  pages={746--774},
  year={2001},
  publisher={Taylor \& Francis}
}


@Manual{network_package,
    title = {network: Classes for Relational Data},
    author = {Carter T. Butts and Mark S. Handcock and David R. Hunter},
    year = {2014},
    address = {Irvine, CA},
    url = {http://statnet.org/},
    note = {R package version 1.10.2}
}


@book{ggplot2_package,
  title={ggplot2: elegant graphics for data analysis},
  author={Wickham, Hadley},
  year={2009},
  publisher={Springer}
}

@article{cytoscape,
  title={Cytoscape: a software environment for integrated models of biomolecular interaction networks},
  author={Shannon, Paul and Markiel, Andrew and Ozier, Owen and Baliga, Nitin S and Wang, Jonathan T and Ramage, Daniel and Amin, Nada and Schwikowski, Benno and Ideker, Trey},
  journal={Genome research},
  volume={13},
  number={11},
  pages={2498--2504},
  year={2003},
  publisher={Cold Spring Harbor Lab}
}




