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@article{Broman:2003fk,
	Abstract = {SUMMARY: R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental populations derived from inbred lines. It is implemented as an add-on package for the freely-available statistical software, R, and includes functions for estimating genetic maps, identifying genotyping errors, and performing single-QTL and two-dimensional, two-QTL genome scans by multiple methods, with the possible inclusion of covariates. AVAILABILITY: The package is freely available at http://www.biostat.jhsph.edu/~kbroman/qtl.},
	Author = {Broman, Karl W and Wu, Hao and Sen, Saunak and Churchill, Gary A},
	Date-Added = {2013-03-25 15:25:40 +0000},
	Date-Modified = {2013-03-25 15:25:40 +0000},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Chromosome Mapping; Chromosomes, Human, Pair 1; Crosses, Genetic; Gene Expression Profiling; Genetic Markers; Genome; Genotype; Humans; Inbreeding; Markov Chains; Models, Genetic; Models, Statistical; Quantitative Trait Loci; Software},
	Month = {May},
	Number = {7},
	Pages = {889-90},
	Pmid = {12724300},
	Pst = {ppublish},
	Title = {R/qtl: QTL mapping in experimental crosses},
	Volume = {19},
	Year = {2003}}

@article{Barrett:2007dq,
	Abstract = {The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely disseminates microarray and other forms of high-throughput data generated by the scientific community. The database has a minimum information about a microarray experiment (MIAME)-compliant infrastructure that captures fully annotated raw and processed data. Several data deposit options and formats are supported, including web forms, spreadsheets, XML and Simple Omnibus Format in Text (SOFT). In addition to data storage, a collection of user-friendly web-based interfaces and applications are available to help users effectively explore, visualize and download the thousands of experiments and tens of millions of gene expression patterns stored in GEO. This paper provides a summary of the GEO database structure and user facilities, and describes recent enhancements to database design, performance, submission format options, data query and retrieval utilities. GEO is accessible at http://www.ncbi.nlm.nih.gov/geo/},
	Address = {National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 45 Center Drive, Bethesda, MD 20892, USA. barrett@ncbi.nlm.nih.gov},
	Au = {Barrett, T and Troup, DB and Wilhite, SE and Ledoux, P and Rudnev, D and Evangelista, C and Kim, IF and Soboleva, A and Tomashevsky, M and Edgar, R},
	Author = {Barrett, Tanya and Troup, Dennis B and Wilhite, Stephen E and Ledoux, Pierre and Rudnev, Dmitry and Evangelista, Carlos and Kim, Irene F and Soboleva, Alexandra and Tomashevsky, Maxim and Edgar, Ron},
	Da = {20070104},
	Date-Added = {2008-07-27 19:39:13 +0200},
	Date-Modified = {2008-09-17 18:38:47 +0200},
	Dcom = {20070316},
	Dep = {20061111},
	Doi = {10.1093/nar/gkl887},
	Edat = {2006/11/14 09:00},
	Issn = {1362-4962 (Electronic)},
	Jid = {0411011},
	Journal = {Nucleic Acids Res},
	Jt = {Nucleic acids research},
	Language = {eng},
	Mh = {Computer Graphics; *Databases, Genetic; *Gene Expression Profiling; Humans; Internet; *Oligonucleotide Array Sequence Analysis; Software; User-Computer Interface},
	Mhda = {2007/03/17 09:00},
	Number = {Database issue},
	Own = {NLM},
	Pages = {D760-5},
	Phst = {2006/11/11 {$[$}aheadofprint{$]$}},
	Pii = {gkl887},
	Pl = {England},
	Pmc = {PMC1669752},
	Pmid = {17099226},
	Pst = {ppublish},
	Pt = {Journal Article; Research Support, N.I.H., Intramural},
	Pubm = {Print-Electronic},
	Sb = {IM},
	So = {Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5. Epub 2006 Nov 11.},
	Stat = {MEDLINE},
	Title = {{NCBI GEO}: mining tens of millions of expression profiles--database and tools update.},
	Volume = {35},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/nar/gkl887}}

@article{Castelo:2006yu,
	Abstract = {Learning of large-scale networks of interactions from microarray data is an important and challenging problem in bioinformatics. A widely used approach is to assume that the available data constitute a random sample from a multivariate distribution belonging to a Gaussian graphical model. As a consequence, the prime objects of inference are full-order partial correlations which are partial correlations between two variables given the remaining ones. In the context of microarray data the number of variables exceed the sample size and this precludes the application of traditional structure learning procedures because a sampling version of full-order partial correlations does not exist. In this paper we consider limited-order partial correlations, these are partial correlations computed on marginal distributions of manageable size, and provide a set of rules that allow one to assess the usefulness of these quantities to derive the independence structure of the underlying Gaussian graphical model. Furthermore, we introduce a novel structure learning procedure based on a quantity, obtained from limited-order partial correlations, that we call the non-rejection rate. The applicability and usefulness of the procedure are demonstrated by both simulated and real data.},
	Author = {Castelo, R. and Roverato, A.},
	Date-Added = {2008-07-21 19:27:53 +0200},
	Date-Modified = {2008-09-30 10:29:55 +0200},
	Issn = {1532-4435},
	Journal = {J Mach Learn Res},
	Pages = {2621--2650},
	Title = {A robust procedure for Gaussian graphical model search from microarray data with p larger than n},
	Volume = {7},
	Year = {2006}}

@article{Castelo:2009fk,
	Abstract = {Reverse engineering bioinformatic procedures applied to high-throughput experimental data have become instrumental in generating new hypotheses about molecular regulatory mechanisms. This has been particularly the case for gene expression microarray data, where a large number of statistical and computational methodologies have been developed in order to assist in building network models of transcriptional regulation. A major challenge faced by every different procedure is that the number of available samples n for estimating the network model is much smaller than the number of genes p forming the system under study. This compromises many of the assumptions on which the statistics of the methods rely, often leading to unstable performance figures. In this work, we apply a recently developed novel methodology based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data with p >> n. Using experimental and functional annotation data from Escherichia coli, here we show how qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, we also show that the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions. An R package, called qpgraph implementing this method is part of the Bioconductor project and can be downloaded from (www.bioconductor.org). A parallel standalone version for the most computationally expensive calculations is available from (http://functionalgenomics.upf.xsedu/qpgraph).},
	Author = {Castelo, Robert and Roverato, Alberto},
	Date-Added = {2010-08-02 15:30:34 +0200},
	Date-Modified = {2010-08-02 15:30:34 +0200},
	Doi = {10.1089/cmb.2008.08TT},
	Journal = {J Comput Biol},
	Journal-Full = {Journal of computational biology : a journal of computational molecular cell biology},
	Mesh = {Algorithms; Computational Biology; Computer Simulation; Escherichia coli; Gene Regulatory Networks; Metabolic Networks and Pathways; Models, Biological; Oligonucleotide Array Sequence Analysis; Software},
	Month = {Feb},
	Number = {2},
	Pages = {213-27},
	Pmid = {19178140},
	Pst = {ppublish},
	Title = {Reverse engineering molecular regulatory networks from microarray data with qp-graphs},
	Volume = {16},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1089/cmb.2008.08TT}}

@article{Covert:2004fx,
	Abstract = {The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.},
	Address = {Bioengineering Department, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0412, USA.},
	Au = {Covert, MW and Knight, EM and Reed, JL and Herrgard, MJ and Palsson, BO},
	Author = {Covert, Markus W and Knight, Eric M and Reed, Jennifer L and Herrgard, Markus J and Palsson, Bernhard O},
	Da = {20040506},
	Date-Added = {2008-07-20 19:17:47 +0200},
	Date-Modified = {2008-07-20 19:17:51 +0200},
	Dcom = {20040521},
	Doi = {10.1038/nature02456},
	Edat = {2004/05/07 05:00},
	Issn = {1476-4687 (Electronic)},
	Jid = {0410462},
	Journal = {Nature},
	Jt = {Nature},
	Language = {eng},
	Lr = {20061115},
	Mh = {Aerobiosis/genetics; Anaerobiosis/genetics; *Computational Biology; *Computer Simulation; Escherichia coli/*genetics/growth \& development/*metabolism; Gene Expression Profiling; Genes, Bacterial/genetics; Models, Biological; Phenotype},
	Mhda = {2004/05/22 05:00},
	Number = {6987},
	Own = {NLM},
	Pages = {92--96},
	Phst = {2003/11/22 {$[$}received{$]$}; 2004/03/01 {$[$}accepted{$]$}},
	Pii = {nature02456},
	Pl = {England},
	Pmid = {15129285},
	Pst = {ppublish},
	Pt = {Journal Article; Research Support, U.S. Gov't, P.H.S.},
	Pubm = {Print},
	Sb = {IM},
	So = {Nature. 2004 May 6;429(6987):92-6.},
	Stat = {MEDLINE},
	Title = {Integrating high-throughput and computational data elucidates bacterial networks.},
	Volume = {429},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature02456}}

@article{Fawcett:2006fj,
	Author = {Fawcett, T.},
	Date-Added = {2008-09-24 14:00:13 +0200},
	Date-Modified = {2008-09-24 18:36:52 +0200},
	Doi = {10.1016/j.patrec.2005.10.010},
	Issn = {0167-8655},
	Issue = {8},
	Journal = {Pattern Recogn Lett},
	Pages = {861--874},
	Title = {An introduction to {ROC} analysis},
	Volume = {27},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.patrec.2005.10.010}}

@article{Gama-Castro:2008le,
	Abstract = {RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference database offering curated knowledge of the transcriptional regulatory network of Escherichia coli K12, currently the best-known electronically encoded database of the genetic regulatory network of any free-living organism. This paper summarizes the improvements, new biology and new features available in version 6.0. Curation of original literature is, from now on, up to date for every new release. All the objects are supported by their corresponding evidences, now classified as strong or weak. Transcription factors are classified by origin of their effectors and by gene ontology class. We have now computational predictions for sigma(54) and five different promoter types of the sigma(70) family, as well as their corresponding -10 and -35 boxes. In addition to those curated from the literature, we added about 300 experimentally mapped promoters coming from our own high-throughput mapping efforts. RegulonDB v.6.0 now expands beyond transcription initiation, including RNA regulatory elements, specifically riboswitches, attenuators and small RNAs, with their known associated targets. The data can be accessed through overviews of correlations about gene regulation. RegulonDB associated original literature, together with more than 4000 curation notes, can now be searched with the Textpresso text mining engine.},
	Address = {Program of Computational Genomics, Centro de Ciencias Genomicas, Universidad Nacional Autonoma de Mexico, A.P. 565-A, Cuernavaca, Morelos 62100, Mexico.},
	Au = {Gama-Castro, S and Jimenez-Jacinto, V and Peralta-Gil, M and Santos-Zavaleta, A and Penaloza-Spinola, MI and Contreras-Moreira, B and Segura-Salazar, J and Muniz-Rascado, L and Martinez-Flores, I and Salgado, H and Bonavides-Martinez, C and Abreu-Goodger, C and Rodriguez-Penagos, C and Miranda-Rios, J and Morett, E and Merino, E and Huerta, AM and Trevino-Quintanilla, L and Collado-Vides, J},
	Author = {Gama-Castro, Socorro and Jimenez-Jacinto, Veronica and Peralta-Gil, Martin and Santos-Zavaleta, Alberto and Penaloza-Spinola, Monica I and Contreras-Moreira, Bruno and Segura-Salazar, Juan and Muniz-Rascado, Luis and Martinez-Flores, Irma and Salgado, Heladia and Bonavides-Martinez, Cesar and Abreu-Goodger, Cei and Rodriguez-Penagos, Carlos and Miranda-Rios, Juan and Morett, Enrique and Merino, Enrique and Huerta, Araceli M and Trevino-Quintanilla, Luis and Collado-Vides, Julio},
	Da = {20080115},
	Date-Added = {2008-07-20 18:44:57 +0200},
	Date-Modified = {2008-09-30 10:30:58 +0200},
	Dcom = {20080317},
	Dep = {20071223},
	Doi = {10.1093/nar/gkm994},
	Edat = {2007/12/26 09:00},
	Gr = {GM071962-03/GM/United States NIGMS},
	Issn = {1362-4962 (Electronic)},
	Jid = {0411011},
	Journal = {Nucleic Acids Res},
	Jt = {Nucleic acids research},
	Language = {eng},
	Mh = {Computational Biology; *Databases, Genetic; Escherichia coli K12/*genetics; *Gene Expression Regulation, Bacterial; *Gene Regulatory Networks; Internet; Models, Genetic; Promoter Regions (Genetics); Regulatory Sequences, Ribonucleic Acid; Regulon; Sigma Factor/metabolism; Software; Transcription Factors/metabolism; Transcription Initiation Site; Transcription, Genetic},
	Mhda = {2008/03/18 09:00},
	Number = {Database issue},
	Own = {NLM},
	Pages = {D120-4},
	Phst = {2007/12/23 {$[$}aheadofprint{$]$}},
	Pii = {gkm994},
	Pl = {England},
	Pmc = {PMC2238961},
	Pmid = {18158297},
	Pst = {ppublish},
	Pt = {Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't},
	Pubm = {Print-Electronic},
	Rn = {0 (Regulatory Sequences, Ribonucleic Acid); 0 (Sigma Factor); 0 (Transcription Factors)},
	Sb = {IM},
	So = {Nucleic Acids Res. 2008 Jan;36(Database issue):D120-4. Epub 2007 Dec 23.},
	Stat = {MEDLINE},
	Title = {Regulon{DB} (version 6.0): gene regulation model of {E}scherichia coli {K}-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation.},
	Volume = {36},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/nar/gkm994}}

@book{Lauritzen:1996fj,
	Author = {Lauritzen, S.L.},
	Date-Added = {2008-09-17 11:38:10 +0200},
	Date-Modified = {2008-09-17 11:38:10 +0200},
	Publisher = {Oxford University Press},
	Title = {Graphical models},
	Year = {1996}}

@book{Harary:1969,
	Author = {Harary, Frank},
	Publisher = {Addison-Wesley},
	Title = {Graph theory},
	Year = {1969}}

@article{Krzywinski:2012,
  author = {Krzywinski, Martin and Birol, Inanc and Jones, Steven JM and Marra, Marco A},
  title = {Hive plots--rational approach to visualizing networks},
  journal = {Brief Bioinform},
  journal-full = {Briefings in Bioinformatics},
  volume = {13},
  number = {5},
  pages = {627-644},
  year = {2012},
  doi = {10.1093/bib/bbr069}}

@manual{Hanson:2014,
  title = {{H}ive{R}: 2{D} and 3{D} {H}ive plots for {R}},
  author = {Hanson, Bryan A},
  year = {2014},
  note = {R/CRAN pkg. ver. 0.2-27},
  web = {http://cran.r-project.org/web/packages/HiveR}
}
