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@ARTICLE{akutsu98,
  author = {Akutsu, T and Kuhara, S and Maruyama, O and Miyano, S},
  title = {{A System for Identifying Genetic Networks from Gene Expression Patterns
	Produced by Gene Disruptions and Overexpressions}},
  journal = {Genome Inform. Ser. Workshop Genome Inform.},
  year = {1998},
  volume = {9},
  pages = {151--160}
}

@ARTICLE{pmid10380182,
  author = {Akutsu, T and Miyano, S and Kuhara, S},
  title = {{{I}dentification of genetic networks from a small number of gene
	expression patterns under the {B}oolean network model}},
  journal = {Pac Symp Biocomput},
  year = {1999},
  pages = {17--28}
}

@ARTICLE{ashburner00:GO,
  author = {Ashburner, M. and Ball, C. A. and Blake, J. A. and Botstein, D. and
	Butler, H. and Cherry, J. M. and Davis, A. P. and Dolinski, K. and
	Dwight, S. S. and Eppig, J. T. and Harris, M. A. and Hill, D. P.
	and Issel-Tarver, L. and Kasarskis, A. and Lewis, S. and Matese,
	J. C. and Richardson, J. E. and Ringwald, M. and Rubin, G. M. and
	Sherlock, G.},
  title = {Gene ontology: tool for the unification of biology. The Gene Ontology
	Consortium},
  journal = {Nat Genet},
  year = {2000},
  volume = {25},
  pages = {25-9},
  number = {1},
  keywords = {Animal Computer Communication Networks Databases, Factual Eukaryotic
	Cells/*physiology *Genes, Structural Human Metaphysics Mice Molecular
	Biology/*trends *Sequence Analysis, DNA Support, Non-U.S. Gov't Support,
	U.S. Gov't, P.H.S. *Terminology}
}

@ARTICLE{pmid7584402,
  author = {Bailey, T L and Elkan, C},
  title = {{Fitting a mixture model by expectation maximization to discover
	motifs in biopolymers}},
  journal = {Proc Int Conf Intell Syst Mol Biol},
  year = {1994},
  volume = {2},
  pages = {28--36}
}

@ARTICLE{pmid15608265,
  author = {Ball, Catherine A and Awad, Ihab A B and Demeter, Janos and Gollub,
	Jeremy and Hebert, Joan M and Hernandez-Boussard, Tina and Jin, Heng
	and Matese, John C and Nitzberg, Michael and Wymore, Farrell and
	Zachariah, Zachariah K and Brown, Patrick O and Sherlock, Gavin},
  title = {{The Stanford Microarray Database accommodates additional microarray
	platforms and data formats}},
  journal = {Nucleic Acids Res},
  year = {2005},
  volume = {33},
  pages = {580--582},
  number = {Database issue},
  month = {Jan}
}

@ARTICLE{barkai00,
  author = {Barkai, N and Leibler, S},
  title = {{Circadian clocks limited by noise}},
  journal = {Nature},
  year = {2000},
  volume = {403},
  pages = {267--268},
  number = {6767},
  month = {Jan}
}

@ARTICLE{basso05,
  author = {Basso, Katia and Margolin, Adam A and Stolovitzky, Gustavo and Klein,
	Ulf and Dalla-Favera, Riccardo and Califano, Andrea},
  title = {{Reverse engineering of regulatory networks in human B cells}},
  journal = {Nat. Genet.},
  year = {2005},
  volume = {37},
  pages = {382--390},
  number = {4},
  month = {Apr}
}

@ARTICLE{baum70:baum-welch,
  author = {L. E. Baum and T. Peterie and G. Souled and N. Weiss},
  title = {A maximization technique occurring in the statistical analysis of
	probabilistic functions of {M}arkov chains.},
  journal = {Ann. Math. Statist.},
  year = {1970},
  volume = { 41},
  pages = {164--171},
  number = {1}
}

@ARTICLE{pmid15353451,
  author = {Beal, Matthew J and Falciani, Francesco and Ghahramani, Zoubin and
	Rangel, Claudia and Wild, David L},
  title = {{A Bayesian approach to reconstructing genetic regulatory networks
	with hidden factors}},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {349--356},
  number = {3},
  month = {Feb},
  note = {Evaluation Studies}
}

@ARTICLE{beer04:_predic,
  author = {Beer, Michael A and Tavazoie, Saeed},
  title = {{Predicting gene expression from sequence}},
  journal = {Cell},
  year = {2004},
  volume = {117},
  pages = {185--198},
  number = {2},
  month = {Apr}
}

@ARTICLE{Bernard2005,
  author = {Allister Bernard and Alexander J Hartemink},
  title = {Informative structure priors: joint learning of dynamic regulatory
	networks from multiple types of data.},
  journal = {Pac Symp Biocomput},
  year = {2005},
  pages = {459--470},
  abstract = {We present a method for jointly learning dynamic models of transcriptional
	regulatory networks from gene expression data and transcription factor
	binding location data. Models are automatically learned using dynamic
	Bayesian network inference algorithms; joint learning is accomplished
	by incorporating evidence from gene expression data through the likelihood,
	and from transcription factor binding location data through the prior.
	We propose a new informative structure prior with two advantages.
	First, the prior incorporates evidence from location data probabilistically,
	allowing it to be weighed against evidence from expression data.
	Second, the prior takes on a factorable form that is computationally
	efficient when learning dynamic regulatory networks. Results obtained
	from both simulated and experimental data from the yeast cell cycle
	demonstrate that this joint learning algorithm can recover dynamic
	regulatory networks from multiple types of data that are more accurate
	than those recovered from each type of data in isolation.},
  keywords = {Bayes Theorem; Cell Cycle; Computer Simulation; Markov Chains; Models,
	Genetic; Probability; Transcription, Genetic},
  owner = {bartek},
  pmid = {15759651},
  timestamp = {2008.02.18}
}

@ARTICLE{dibernardo05,
  author = {di Bernardo, Diego and Thompson, Michael J and Gardner, Timothy S
	and Chobot, Sarah E and Eastwood, Erin L and Wojtovich, Andrew P
	and Elliott, Sean J and Schaus, Scott E and Collins, James J},
  title = {{Chemogenomic profiling on a genome-wide scale using reverse-engineered
	gene networks}},
  journal = {Nat. Biotechnol.},
  year = {2005},
  volume = {23},
  pages = {377--383},
  number = {3},
  month = {Mar},
  nnote = {Evaluation Studies}
}

@ARTICLE{brown00:_explor_dna,
  author = {Brown, P.O. and Botstein, D.},
  title = {Exploring the new world of the genome with DNA microarrays.},
  journal = {Nature Genet.},
  year = {2000},
  volume = {21},
  pages = {33-37}
}

@ARTICLE{brzeski03:_defic_dna_methy_ddm1,
  author = {Brzeski, J. and Jerzmanowski, A.},
  title = {Deficient in DNA Methylation 1 (DDM1) defines a novel family of chromatin
	remodeling factors.},
  journal = {J. Biol. Chem.},
  year = {2003},
  volume = {278},
  number = {823-828}
}

@ARTICLE{brzeski03:_plant,
  author = {Brzeski, J., Dyczkowski, J., Kaczanowski, S., Zielenkiewicz, P. and
	Jerzmanowski, A},
  title = {Plant chromatin - learning from similarities and differences.},
  journal = {Adv. in Botanical Res},
  year = {2003},
  volume = {40},
  pages = {107-142}
}

@ARTICLE{brzeski99:_ident_arabid_bsh_snf5,
  author = {Brzeski, J., Podstolski, W., Olczak, K., and Jerzmanowski, A.},
  title = {Identification and analysis of Arabidopsis thaliana BSH gene, a member
	of SNF5 gene family.},
  journal = {Nucleic Acids Res.},
  year = {1999},
  volume = {27},
  pages = {2393-2399}
}

@ARTICLE{Chang1998,
  author = {Wendy Chang and Deepak Murthy and Aidong Zhang and Tanveer F. Syeda-mahmood},
  title = {Global Integration of Visual Databases},
  year = {1998},
  citeseercitationcount = {0},
  citeseerurl = {http://citeseer.ist.psu.edu/2001.html},
  owner = {bartek},
  timestamp = {2008.02.18}
}

@ARTICLE{pmid16386357,
  author = {Chaouiya, Claudine and de Jong, Hidde and Thieffry, Denis},
  title = {{{D}ynamical modeling of biological regulatory networks}},
  journal = {Biosystems},
  year = {2006},
  volume = {84},
  pages = {77--80},
  number = {2},
  month = {May},
  note = {Editorial}
}

@ARTICLE{chen04:_integ,
  author = {Chen, K.C. and Calzone, L. and Csikasz-Nagy, A. and Cross, F.R. and
	Novak, B. and Tyson, J.J.},
  title = {Integrative analysis of cell cycle control in budding yeast.},
  journal = {Molecular Biology of the Cell},
  year = {2004}
}

@ARTICLE{chen00:_kinet,
  author = {Chen, K.C. and Csikasz-Nagy, A. and Gyorffy, B. and Val, J. and Novak,
	B. and Tyson, J.J.},
  title = {Kinetic analysis of a molecular model of the budding yeast cell cycle.},
  journal = {Molecular Biology of the Cell},
  year = {2000}
}

@ARTICLE{chen05:SDE,
  author = {Chen, KC and Wang, TY and Tseng, HH and Huang, CY and Kao, CY},
  title = {A stochastic differential equation model for quantifying transcriptional
	regulatory network in {S}accharomyces cerevisiae.},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {2883-90},
  number = {12},
  month = {Jun}
}

@ARTICLE{pmid15802287,
  author = {Chen, Kuang-Chi and Wang, Tse-Yi and Tseng, Huei-Hun and Huang, Chi-Ying
	F and Kao, Cheng-Yan},
  title = {{{A} stochastic differential equation model for quantifying transcriptional
	regulatory network in {S}accharomyces cerevisiae}},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {2883--2890},
  number = {12},
  month = {Jun},
  note = {Evaluation Studies}
}

@ARTICLE{pmid10977070,
  author = {Cheng, Y and Church, G M},
  title = {{Biclustering of expression data}},
  journal = {Proc Int Conf Intell Syst Mol Biol},
  year = {2000},
  volume = {8},
  pages = {93--103}
}

@ARTICLE{cherry00,
  author = {Cherry, J L and Adler, F R},
  title = {{How to make a biological switch}},
  journal = {J. Theor. Biol.},
  year = {2000},
  volume = {203},
  pages = {117--133},
  number = {2},
  month = {Mar}
}

@ARTICLE{chickering1995lbn,
  author = {Chickering, D.M.},
  title = {Learning {B}ayesian networks is {NP}-complete},
  journal = {Proceedings of AI and Statistics},
  year = {1995},
  volume = {1995}
}

@INCOLLECTION{chickering96,
  author = {Chickering, D. M.},
  title = {{Learning Bayesian networks is NP-complete}},
  booktitle = {Learning from Data: Artificial Inteligence and Statistics V},
  publisher = {Springer-Verlag},
  year = {1996},
  editor = {Fisher, D. and Lenz, H.-J.}
}

@ARTICLE{chickering04,
  author = {David Maxwell Chickering and David Heckerman and Christopher Meek},
  title = {Large-Sample Learning of {B}ayesian Networks is {NP}-Hard},
  journal = {Journal of Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {1287--1330},
  month = {Oct},
  uurl = {http://www.jmlr.org/papers/v5/chickering04a.html}
}

@ARTICLE{Cho98:cell_cycle,
  author = {Cho, R. J. and Campbell, M. J. and Winzeler, E. A. and Steinmetz,
	L. and Conway, A. and Wodicka, L. and Wolfsberg, T. G. and Gabrielian,
	A. E. and Landsman, D. and Lockhart, D. J. and Davis, R. W.},
  title = {A genome-wide transcriptional analysis of the mitotic cell cycle},
  journal = {Mol Cell},
  year = {1998},
  volume = {2},
  pages = {65-73},
  number = {1},
  abstract = {Progression through the eukaryotic cell cycle is known to be both
	regulated and accompanied by periodic fluctuation in the expression
	levels of numerous genes. We report here the genome-wide characterization
	of mRNA transcript levels during the cell cycle of the budding yeast
	S. cerevisiae. Cell cycle-dependent periodicity was found for 416
	of the 6220 monitored transcripts. More than 25% of the 416 genes
	were found directly adjacent to other genes in the genome that displayed
	induction in the same cell cycle phase, suggesting a mechanism for
	local chromosomal organization in global mRNA regulation. More than
	60% of the characterized genes that displayed mRNA fluctuation have
	already been implicated in cell cycle period-specific biological
	roles. Because more than 20% of human proteins display significant
	homology to yeast proteins, these results also link a range of human
	genes to cell cycle period-specific biological functions.},
  keywords = {Cell Cycle Chromosome Mapping Chromosomes, Fungal/*genetics DNA, Fungal/genetics
	*Gene Expression Regulation, Fungal *Genome, Fungal Mitosis/*genetics
	RNA, Fungal/*biosynthesis/genetics RNA, Messenger/*biosynthesis/genetics
	Saccharomyces cerevisiae/cytology/*genetics/metabolism Support, U.S.
	Gov't, P.H.S. *Transcription, Genetic}
}

@ARTICLE{pmid11137997,
  author = {Cho, R J and Huang, M and Campbell, M J and Dong, H and Steinmetz,
	L and Sapinoso, L and Hampton, G and Elledge, S J and Davis, R W
	and Lockhart, D J},
  title = {{Transcriptional regulation and function during the human cell cycle}},
  journal = {Nat Genet},
  year = {2001},
  volume = {27},
  pages = {48--54},
  number = {1},
  month = {Jan}
}

@ARTICLE{chu98:sporul,
  author = {Chu, S. and DeRisi, J. and Eisen, M. and Mulholland, J. and Botstein,
	D. and Brown, P. O. and Herskowitz, I.},
  title = {The transcriptional program of sporulation in budding yeast},
  journal = {Science},
  year = {1998},
  volume = {282},
  pages = {699-705},
  number = {5389},
  abstract = {Diploid cells of budding yeast produce haploid cells through the developmental
	program of sporulation, which consists of meiosis and spore morphogenesis.
	DNA microarrays containing nearly every yeast gene were used to assay
	changes in gene expression during sporulation. At least seven distinct
	temporal patterns of induction were observed. The transcription factor
	Ndt80 appeared to be important for induction of a large group of
	genes at the end of meiotic prophase. Consensus sequences known or
	proposed to be responsible for temporal regulation could be identified
	solely from analysis of sequences of coordinately expressed genes.
	The temporal expression pattern provided clues to potential functions
	of hundreds of previously uncharacterized genes, some of which have
	vertebrate homologs that may function during gametogenesis.},
  keywords = {Animal Chromosomes, Fungal/physiology Fungal Proteins/genetics/metabolism
	*Gene Expression Regulation, Fungal Genes, Fungal Genome, Fungal
	Human Meiosis/*genetics Morphogenesis Organelles/ultrastructure Saccharomyces
	cerevisiae/*genetics/physiology Spores, Fungal/*genetics/physiology/ultrastructure
	Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Transcription
	Factors/genetics/metabolism *Transcription, Genetic}
}

@ARTICLE{cooper92,
  author = {Cooper, Gregory F. and Herskovits, Edward},
  title = {A {B}ayesian Method for the Induction of Probabilistic Networks from
	Data},
  journal = {Machine Learning},
  year = {1992},
  volume = {9},
  pages = {309-347},
  uurl = {http://dx.doi.org/10.1023/A:1022649401552}
}

@ARTICLE{Testing_small_binom,
  author = {D'Agostino, R.B. and Chase, W. and Belanger, A.},
  title = {The appropriateness of Some Common proceduresfor Testing the Equality
	of Two Independent Binomial Populations},
  journal = {The American Statistician},
  year = {1988},
  volume = {42},
  pages = {198-202},
  number = {3},
  month = {Aug.}
}

@ARTICLE{pmid16884529,
  author = {Dabrowski, Michal and Aerts, Stein and Kaminska, Bozena},
  title = {{{P}rediction of a key role of motifs binding {E}2{F} and {N}{R}2{F}
	in down-regulation of numerous genes during the development of the
	mouse hippocampus}},
  journal = {BMC Bioinformatics},
  year = {2006},
  volume = {7},
  pages = {367}
}

@INPROCEEDINGS{dasgupta99,
  author = {Sanjoy Dasgupta },
  title = {Learning polytrees},
  booktitle = {Proceedings of the 15th Annual Conference on Uncertainty in Artificial
	Intelligence (UAI-99)},
  year = {1999},
  pages = {134-141},
  address = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@ARTICLE{davidson02,
  author = {Eric H. Davidson and Rast and Oliveri and Ransick and Calestani and
	Yuh and Minokawa and Amore and Hinman and Arenas-Mena and Otim and
	Brown and Livi and Lee and Revilla and Rust and Pan and Schilstra
	and Clarke and Arnone and Rowen and Cameron and McClay and Hood and
	Bolouri and Davidson EH},
  title = {A genomic regulatory network for development.},
  journal = {Science},
  year = {2002}
}

@ARTICLE{DeRisi97:arrays,
  author = {DeRisi, J. L. and Iyer, V. R. and Brown, P. O.},
  title = {Exploring the metabolic and genetic control of gene expression on
	a genomic scale},
  journal = {Science},
  year = {1997},
  volume = {278},
  pages = {680-6},
  number = {5338},
  abstract = {DNA microarrays containing virtually every gene of Saccharomyces cerevisiae
	were used to carry out a comprehensive investigation of the temporal
	program of gene expression accompanying the metabolic shift from
	fermentation to respiration. The expression profiles observed for
	genes with known metabolic functions pointed to features of the metabolic
	reprogramming that occur during the diauxic shift, and the expression
	patterns of many previously uncharacterized genes provided clues
	to their possible functions. The same DNA microarrays were also used
	to identify genes whose expression was affected by deletion of the
	transcriptional co-repressor TUP1 or overexpression of the transcriptional
	activator YAP1. These results demonstrate the feasibility and utility
	of this approach to genomewide exploration of gene expression patterns.},
  keywords = {Citric Acid Cycle Culture Media DNA-Binding Proteins/genetics/metabolism
	Fermentation Fungal Proteins/genetics/metabolism *Gene Expression
	Regulation, Fungal Genes, Fungal Genes, Regulator *Genome, Fungal
	Gluconeogenesis Glucose/metabolism Glyoxylates/metabolism Open Reading
	Frames Oxygen Consumption RNA, Fungal/genetics/metabolism RNA, Messenger/genetics/metabolism
	Repressor Proteins/genetics/metabolism Saccharomyces cerevisiae/*genetics/growth
	& development/*metabolism Support, Non-U.S. Gov't Support, U.S. Gov't,
	P.H.S. Transcription Factors/genetics/metabolism}
}

@INPROCEEDINGS{dojer06,
  author = {Dojer, Norbert},
  title = {{Learning Bayesian Networks Does Not Have to Be NP-Hard}},
  booktitle = {Proceedings of Mathematical Foundations of Computer Science 2006},
  year = {2006},
  editor = {Kr\'alovic, Rastislav and Urzyczyn, Pawe\l},
  pages = {305--314},
  publisher = {Springer-Verlag},
  note = {LNCS 4162}
}

@ARTICLE{dojer10,
  author = {Dojer, Norbert},
  title = {An Efficient Algorithm for Learning Bayesian Networks from Data},
  journal = {Fundamenta Informaticae},
  year = {2010},
  volume = {103},
  pages = {53--67},
  number = {1},
  month = jan,
  abstract = {We propose an algorithm for learning an optimal Bayesian network from
	data. Our method is addressed to biological applications, where usually
	datasets are small, and sets of random variables are large. Moreover,
	we assume that there is no need to examine the acyclicity of the
	graph. We provide polynomial bounds (with respect to the number of
	random variables) for time complexity of our algorithm for two generally
	used scoring criteria: Minimal Description Length and Bayesian-Dirichlet
	equivalence. Then we show how to adapt these criteria to work with
	continuous data and prove polynomial bounds for adapted scores. Finally,
	we briefly describe applications of proposed algorithm in computational
	biology.},
  doi = {10.3233/FI-2010-318},
  owner = {norbert},
  timestamp = {2012.09.07},
  url = {http://dx.doi.org/10.3233/FI-2010-318}
}

@ARTICLE{dojer06:BMC,
  author = {Dojer, Norbert and Gambin, Anna and Wilczy\'{n}ski, Bartek and Tiuryn,
	Jerzy},
  title = {Applying Dynamic {B}ayesian Networks to Perturbed Gene Expression
	Data},
  journal = {BMC Bioinformatics},
  year = {2006},
  volume = {7},
  pages = {249},
  page = {295}
}

@TECHREPORT{dojer05:DBN_tech_rep,
  author = {Dojer, Norbert and Gambin, Anna and Wilczy\'{n}ski, Bartek and Tiuryn,
	Jerzy},
  title = {Applying Dynamic {B}ayesian Networks to Perturbed Gene Expression
	Data},
  institution = {Institute of Informatics, Warsaw University},
  year = {2005}
}

@ARTICLE{drew01,
  author = {D.A. Drew},
  title = {A mathematical model of for prokaryotic protin synthesis},
  journal = {Bull. Math. Biol.},
  year = {2001}
}

@ARTICLE{eisen98:clusters,
  author = {Eisen, M. B. and Spellman, P. T. and Brown, P. O. and Botstein, D.},
  title = {Cluster analysis and display of genome-wide expression patterns},
  journal = {Proc Natl Acad Sci U S A},
  year = {1998},
  volume = {95},
  pages = {14863-8},
  number = {25},
  abstract = {A system of cluster analysis for genome-wide expression data from
	DNA microarray hybridization is described that uses standard statistical
	algorithms to arrange genes according to similarity in pattern of
	gene expression. The output is displayed graphically, conveying the
	clustering and the underlying expression data simultaneously in a
	form intuitive for biologists. We have found in the budding yeast
	Saccharomyces cerevisiae that clustering gene expression data groups
	together efficiently genes of known similar function, and we find
	a similar tendency in human data. Thus patterns seen in genome-wide
	expression experiments can be interpreted as indications of the status
	of cellular processes. Also, coexpression of genes of known function
	with poorly characterized or novel genes may provide a simple means
	of gaining leads to the functions of many genes for which information
	is not available currently.},
  keywords = {Cluster Analysis Gene Expression *Genome, Fungal *Genome, Human Human
	*Multigene Family Saccharomyces cerevisiae/*genetics Support, U.S.
	Gov't, P.H.S.}
}

@BOOK{xppaut,
  title = {Simulating, Analyzing, and Animating Dynamical Systems: A Guide to
	Xppaut for Researchers and Students},
  publisher = {Society for Industrial and Applied Mathematics},
  year = {2002},
  author = {Bard Ermentrout},
  edition = {1}
}

@ARTICLE{pmid12169566,
  author = {Eskin, Eleazar and Pevzner, Pavel A},
  title = {{Finding composite regulatory patterns in DNA sequences}},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18 Suppl 1},
  pages = {354--363}
}

@ARTICLE{pmid16873462,
  author = {Faure, Adrien and Naldi, Aurelien and Chaouiya, Claudine and Thieffry,
	Denis},
  title = {{{D}ynamical analysis of a generic {B}oolean model for the control
	of the mammalian cell cycle}},
  journal = {Bioinformatics},
  year = {2006},
  volume = {22},
  pages = {124--131},
  number = {14},
  month = {Jul}
}

@BOOK{filippow88:_diff_eq_discon_rhs,
  title = {Differential Equations with Discontinuous Righthand Sides},
  publisher = {Kluwer Academic Publishers},
  year = {1988},
  author = {A. F. Filippow}
}

@ARTICLE{friedman04,
  author = {Friedman, Nir},
  title = {{Inferring cellular networks using probabilistic graphical models}},
  journal = {Science},
  year = {2004},
  volume = {303},
  pages = {799--805},
  number = {5659},
  month = {Feb}
}

@ARTICLE{friedman03,
  author = {Friedman, Nir and Koller, Daphne},
  title = {Being Bayesian About Network Structure. A Bayesian Approach to Structure
	Discovery in Bayesian Networks},
  journal = {Machine Learning},
  year = {2003},
  volume = {50},
  pages = {95-125},
  owner = {norbert},
  timestamp = {2008.04.30}
}

@ARTICLE{friedman00,
  author = {Friedman, N and Linial, M and Nachman, I and Pe'er, D},
  title = {{Using Bayesian networks to analyze expression data}},
  journal = {J. Comput. Biol.},
  year = {2000},
  volume = {7},
  pages = {601--620},
  number = {3-4}
}

@ARTICLE{friedman00:_using_bayes,
  author = {Nir Friedman and Michal Linial and Iftach Nachman and Dana Pe'er},
  title = {Using Bayesian networks to analyze expression data},
  journal = {journal of Computational Biology},
  year = {2000},
  volume = {7},
  pages = {601-620}
}

@INPROCEEDINGS{friedman98,
  author = {Friedman, N and Murphy, K and Russell, S},
  title = {{Learning the structure of dynamic probabilistic networks}},
  booktitle = {Proceedings of the Fourteenth Conference on Uncertainty in Artificial
	Inteligence},
  year = {1998},
  editor = {Cooper, G F and Moral, S},
  pages = {139--147}
}

@ARTICLE{galper93:_knowl_dna,
  author = {Galper, A.R. and Brutlag, D.L and Millis, D.H.},
  title = {Knowledge-based simulation of DNA metabolism: prediction ofaction
	and envisionment of of pathways.},
  journal = {Artificial Inteligence and Molecular Biology},
  year = {1993}
}

@ARTICLE{gardner03,
  author = {Gardner, Timothy S and di Bernardo, Diego and Lorenz, David and Collins,
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  title = {Exploring the conditional coregulation of yeast gene expression through
	fuzzy k-means clustering},
  journal = {Genome Biol},
  year = {2002},
  volume = {3},
  pages = {RESEARCH0059},
  number = {11},
  abstract = {BACKGROUND: Organisms simplify the orchestration of gene expression
	by coregulating genes whose products function together in the cell.
	Many proteins serve different roles depending on the demands of the
	organism, and therefore the corresponding genes are often coexpressed
	with different groups of genes under different situations. This poses
	a challenge in analyzing whole-genome expression data, because many
	genes will be similarly expressed to multiple, distinct groups of
	genes. Because most commonly used analytical methods cannot appropriately
	represent these relationships, the connections between conditionally
	coregulated genes are often missed. RESULTS: We used a heuristically
	modified version of fuzzy k-means clustering to identify overlapping
	clusters of yeast genes based on published gene-expression data following
	the response of yeast cells to environmental changes. We have validated
	the method by identifying groups of functionally related and coregulated
	genes, and in the process we have uncovered new correlations between
	yeast genes and between the experimental conditions based on similarities
	in gene-expression patterns. To investigate the regulation of gene
	expression, we correlated the clusters with known transcription factor
	binding sites present in the genes' promoters. These results give
	insights into the mechanism of the regulation of gene expression
	in yeast cells responding to environmental changes. CONCLUSIONS:
	Fuzzy k-means clustering is a useful analytical tool for extracting
	biological insights from gene-expression data. Our analysis presented
	here suggests that a prevalent theme in the regulation of yeast gene
	expression is the condition-specific coregulation of overlapping
	sets of genes.},
  keywords = {Binding Sites/genetics *Cluster Analysis Computational Biology/statistics
	& numerical data Databases, Genetic/statistics & numerical data Enhancer
	Elements (Genetics)/genetics Environment Gene Expression Profiling/statistics
	& numerical data Gene Expression Regulation, Fungal/*genetics Genes,
	Structural, Fungal/*genetics Models, Statistical Multigene Family/genetics
	Promoter Regions (Genetics)/genetics Saccharomyces cerevisiae/*genetics
	Support, Non-U.S. Gov't Support, U.S. Gov't, Non-P.H.S. Transcription
	Factors}
}

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	the genome in living cells. Just as maps of metabolic networks describe
	the potential pathways that may be used by a cell to accomplish metabolic
	processes, this network of regulator-gene interactions describes
	potential pathways yeast cells can use to regulate global gene expression
	programs. We use this information to identify network motifs, the
	simplest units of network architecture, and demonstrate that an automated
	process can use motifs to assemble a transcriptional regulatory network
	structure. Our results reveal that eukaryotic cellular functions
	are highly connected through networks of transcriptional regulators
	that regulate other transcriptional regulators.},
  keywords = {Algorithms Cell Cycle Computational Biology DNA, Fungal/genetics/metabolism
	Feedback, Biochemical Gene Expression Profiling *Gene Expression
	Regulation, Fungal *Genes, Fungal Genome, Fungal Models, Genetic
	*Promoter Regions (Genetics) Protein Binding Saccharomyces cerevisiae/cytology/*genetics/growth
	& development/metabolism Saccharomyces cerevisiae Proteins/genetics/*metabolism
	Support, Non-U.S. Gov't Support, U.S. Gov't, Non-P.H.S. Support,
	U.S. Gov't, P.H.S. Transcription Factors/genetics/*metabolism Transcription,
	Genetic}
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@OTHER{BN_soft,
  author = {Kevin Murphy},
  title = {Software Packages for Graphical Models -- Bayesian Networks},
  url = {http://www.cs.ubc.ca/~murphyk/Bayes/bnsoft.html},
  year = {2005}
}

@ARTICLE{murphy1999mge,
  author = {Murphy, K. and Mian, S.},
  title = {Modelling gene expression data using dynamic {B}ayesian networks},
  journal = {University of California, Berkeley},
  year = {1999}
}

@TECHREPORT{murphy02,
  author = {Murphy, K P},
  title = {{Bayes Net Toolbox}},
  institution = {MIT Artificial Intelligence Laboratory},
  year = {2002}
}

@TECHREPORT{murphy99,
  author = {Murphy, K P and Mian, S},
  title = {{Modelling Gene Expression Data Using Dynamic Bayesian Networks}},
  institution = {MIT Artificial Intelligence Laboratory},
  year = {1999}
}

@BOOK{neapolitan03,
  title = {Learning Bayesian Networks},
  publisher = {Prentice Hall},
  year = {2003},
  author = {Neapolitan, Richard E.}
}

@ARTICLE{pmid15363676,
  author = {Novak, Bela and Tyson, John J},
  title = {{{A} model for restriction point control of the mammalian cell cycle}},
  journal = {J Theor Biol},
  year = {2004},
  volume = {230},
  pages = {563--579},
  number = {4},
  month = {Oct}
}

@ARTICLE{ong02,
  author = {Ong, I and Glasner, J and Page, D},
  title = {{Modelling regulatory pathways in \emph{E. coli} from time series
	expression profiles}},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18 Suppl 1},
  pages = {S241-S248}
}

@INPROCEEDINGS{ott04:_optim_model,
  author = {S. Ott and S. Imoto and S. Miyano},
  title = {Finding Optimal Models for Gene Networks },
  booktitle = {Proc. of Pacific Symposium in Biocomputing},
  year = {2004, in press}
}

@ARTICLE{ott04,
  author = {Ott, S and Imoto, S and Miyano, S},
  title = {{Finding optimal models for small gene networks}},
  journal = {Pac. Symp. Biocomput.},
  year = {2004},
  pages = {557--567}
}

@ARTICLE{pmid15608260,
  author = {Parkinson, H and Sarkans, U and Shojatalab, M and Abeygunawardena,
	N and Contrino, S and Coulson, R and Farne, A and Lara, G Garcia
	and Holloway, E and Kapushesky, M and Lilja, P and Mukherjee, G and
	Oezcimen, A and Rayner, T and Rocca-Serra, P and Sharma, A and Sansone,
	S and Brazma, A},
  title = {{ArrayExpress--a public repository for microarray gene expression
	data at the EBI}},
  journal = {Nucleic Acids Res},
  year = {2005},
  volume = {33},
  pages = {553--555},
  number = {Database issue},
  month = {Jan}
}

@ARTICLE{pmid15215380,
  author = {Pavesi, Giulio and Mereghetti, Paolo and Mauri, Giancarlo and Pesole,
	Graziano},
  title = {{Weeder Web: discovery of transcription factor binding sites in a
	set of sequences from co-regulated genes}},
  journal = {Nucleic Acids Res},
  year = {2004},
  volume = {32},
  pages = {199--203},
  number = {Web Server issue},
  month = {Jul}
}

@BOOK{pawlak91:rough_sets,
  title = {Rough sets : theoretical aspects of reasoning about data},
  publisher = {Kluwer Academic Publishers},
  year = {1991},
  author = {Pawlak, Zdzislaw},
  series = {Theory and decision library. Series D, System theory, knowledge engineering,
	and problem solving ; v. 9},
  address = {Dordrecht ; Boston},
  keywords = {Artificial intelligence. Knowledge representation (Information theory)
	Set theory. Numerical analysis Data processing.}
}

@ARTICLE{pawlak82:rough_sets,
  author = {Pawlak, Z.},
  title = {Rough sets.},
  journal = {International journal of Computer and Information Sciences},
  year = {1982},
  volume = {11},
  pages = {341--356}
}

@ARTICLE{peer01,
  author = {Pe'er, D and Regev, A and Elidan, G and Friedman, N},
  title = {{Inferring subnetworks from perturbed expression profiles}},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17 Suppl 1},
  pages = {215--224}
}

@BOOK{pearl88:_probab_reason_intel_system,
  title = {Probabilistic Reasoning in Intelligent Systems},
  publisher = {Morgan-Kauffman},
  year = {1988},
  author = {Judea Pearl },
  address = {San Francisco, CA}
}

@ARTICLE{perrin03,
  author = {Perrin, B-E and Ralaivola, L and Mazurie, A and Bottani, S and Mallet,
	J and D'Alche-Buc, F},
  title = {{Gene networks inference using dynamic Bayesian networks}},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19 Suppl 2},
  pages = {II138-II148}
}

@ARTICLE{pmid16204087,
  author = {Pham, Tho Hoan and Clemente, Jose Carlos and Satou, Kenji and Ho,
	Tu Bao},
  title = {{Computational discovery of transcriptional regulatory rules}},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21 Suppl 2},
  pages = {ii101-ii107},
  month = {Sep}
}

@ARTICLE{pilpel01:mot_comb,
  author = {Pilpel, Y. and Sudarsanam, P. and Church, G. M.},
  title = {Identifying regulatory networks by combinatorial analysis of promoter
	elements},
  journal = {Nat Genet},
  year = {2001},
  volume = {29},
  pages = {153-9},
  number = {2},
  abstract = {Several computational methods based on microarray data are currently
	used to study genome-wide transcriptional regulation. Few studies,
	however, address the combinatorial nature of transcription, a well-established
	phenomenon in eukaryotes. Here we describe a new approach using microarray
	data to uncover novel functional motif combinations in the promoters
	of Saccharomyces cerevisiae. In addition to identifying novel motif
	combinations that affect expression patterns during the cell cycle,
	sporulation and various stress responses, we observed regulatory
	cross-talk among several of these processes. We have also generated
	motif-association maps that provide a global view of transcription
	networks. The maps are highly connected, suggesting that a small
	number of transcription factors are responsible for a complex set
	of expression patterns in diverse conditions. This approach may be
	useful for modeling transcriptional regulatory networks in more complex
	eukaryotes.},
  keywords = {Cell Cycle Computational Biology DNA, Fungal/genetics Gene Expression
	Profiling Oligonucleotide Array Sequence Analysis *Promoter Regions
	(Genetics) Regulatory Sequences, Nucleic Acid Saccharomyces cerevisiae/cytology/genetics/physiology
	Spores, Fungal Support, Non-U.S. Gov't Support, U.S. Gov't, Non-P.H.S.
	Transcription, Genetic}
}

@ARTICLE{pmid11743722,
  author = {Qian, J and Dolled-Filhart, M and Lin, J and Yu, H and Gerstein,
	M},
  title = {{{B}eyond synexpression relationships: local clustering of time-shifted
	and inverted gene expression profiles identifies new, biologically
	relevant interactions}},
  journal = {J Mol Biol},
  year = {2001},
  volume = {314},
  pages = {1053--1066},
  number = {5},
  month = {Dec}
}

@ARTICLE{rabiner89:_tutor_hmm,
  author = {Lawrence R. Rabiner},
  title = {A tutorial on {H}idden {M}arkov {M}odels and selected applications
	in speech recognition.},
  journal = {Proceedings of the IEEE},
  year = {1989},
  volume = {77},
  pages = {257-286},
  number = {2}
}

@ARTICLE{roberts00:signaling,
  author = {Roberts, C. J. and Nelson, B. and Marton, M. J. and Stoughton, R.
	and Meyer, M. R. and Bennett, H. A. and He, Y. D. and Dai, H. and
	Walker, W. L. and Hughes, T. R. and Tyers, M. and Boone, C. and Friend,
	S. H.},
  title = {Signaling and circuitry of multiple MAPK pathways revealed by a matrix
	of global gene expression profiles},
  journal = {Science},
  year = {2000},
  volume = {287},
  pages = {873-80},
  number = {5454},
  abstract = {Genome-wide transcript profiling was used to monitor signal transduction
	during yeast pheromone response. Genetic manipulations allowed analysis
	of changes in gene expression underlying pheromone signaling, cell
	cycle control, and polarized morphogenesis. A two-dimensional hierarchical
	clustered matrix, covering 383 of the most highly regulated genes,
	was constructed from 46 diverse experimental conditions. Diagnostic
	subsets of coexpressed genes reflected signaling activity, cross
	talk, and overlap of multiple mitogen-activated protein kinase (MAPK)
	pathways. Analysis of the profiles specified by two different MAPKs-Fus3p
	and Kss1p-revealed functional overlap of the filamentous growth and
	mating responses. Global transcript analysis reflects biological
	responses associated with the activation and perturbation of signal
	transduction pathways.},
  keywords = {Fungal Proteins/genetics/metabolism/physiology G1 Phase *Gene Expression
	Profiling *Gene Expression Regulation, Fungal Genome, Fungal Lipoproteins/pharmacology/physiology
	*MAP Kinase Signaling System Mitogen-Activated Protein Kinases/metabolism
	Multigene Family Oligonucleotide Array Sequence Analysis Peptides/pharmacology/physiology
	Protein Kinase C/metabolism Saccharomyces cerevisiae/cytology/*genetics/growth
	& development/physiology Support, Non-U.S. Gov't Trans-Activation
	(Genetics) Transcription Factors/metabolism}
}

@ARTICLE{roth98:alignace,
  author = {Roth, F. P. and Hughes, J. D. and Estep, P. W. and Church, G. M.},
  title = {Finding DNA regulatory motifs within unaligned noncoding sequences
	clustered by whole-genome mRNA quantitation},
  journal = {Nat Biotechnol},
  year = {1998},
  volume = {16},
  pages = {939-45},
  number = {10},
  abstract = {Whole-genome mRNA quantitation can be used to identify the genes that
	are most responsive to environmental or genotypic change. By searching
	for mutually similar DNA elements among the upstream non-coding DNA
	sequences of these genes, we can identify candidate regulatory motifs
	and corresponding candidate sets of coregulated genes. We have tested
	this strategy by applying it to three extensively studied regulatory
	systems in the yeast Saccharomyces cerevisiae: galactose response,
	heat shock, and mating type. Galactose-response data yielded the
	known binding site of Gal4, and six of nine genes known to be induced
	by galactose. Heat shock data yielded the cell-cycle activation motif,
	which is known to mediate cell-cycle dependent activation, and a
	set of genes coding for all four nucleosomal proteins. Mating type
	alpha and a data yielded all of the four relevant DNA motifs and
	most of the known a- and alpha-specific genes.},
  keywords = {Cloning, Molecular DNA/*genetics Galactose/metabolism Gene Expression
	Regulation, Fungal Glucose/metabolism Peptides/genetics RNA, Messenger/*genetics
	*Regulatory Sequences, Nucleic Acid Saccharomyces cerevisiae/genetics/growth
	& development Support, U.S. Gov't, Non-P.H.S.}
}

@ARTICLE{pmid15845847,
  author = {Sachs, K. and Perez, O. and Pe'er, D. and Lauffenburger, D.A. and
	Nolan, G.P.},
  title = {{C}ausal protein-signaling networks derived from multiparameter single-cell
	data},
  journal = {Science},
  year = {2005},
  volume = {308},
  pages = {523--529},
  month = {Apr},
  owner = {wilczyns},
  timestamp = {2008.06.16}
}

@ARTICLE{pmid9446747,
  author = {Sanchez, L and van Helden, J and Thieffry, D},
  title = {{Establishement of the dorso-ventral pattern during embryonic development
	of drosophila melanogasater: a logical analysis}},
  journal = {Journal of Theoretical Biology},
  year = {1997},
  volume = {189},
  pages = {377--389},
  number = {4},
  month = {Dec}
}

@ARTICLE{pmid12957124,
  author = {Sanchez, Lucas and Thieffry, Denis},
  title = {{Segmenting the fly embryo: a logical analysis of the pair-rule cross-regulatory
	module}},
  journal = {Journal of Theoretical Biology},
  year = {2003},
  volume = {224},
  pages = {517--537},
  number = {4},
  month = {Oct}
}

@ARTICLE{pmid11419955,
  author = {L. Sanchez and D. Thieffry},
  title = {{A logical analysis of the {D}rosophila gap-gene system}},
  journal = {Journal of Theoretical Biology},
  year = {2001},
  volume = {211},
  pages = {115--141},
  number = {2},
  month = {Jul}
}

@ARTICLE{pmid16055636,
  author = {Sarnowski, Tomasz J and Rios, Gabino and Jasik, Jan and Swiezewski,
	Szymon and Kaczanowski, Szymon and Li, Yong and Kwiatkowska, Aleksandra
	and Pawlikowska, Katarzyna and Kozbial, Marta and Kozbial, Piotr
	and Koncz, Csaba and Jerzmanowski, Andrzej},
  title = {{{S}{W}{I}3 subunits of putative {S}{W}{I}/{S}{N}{F} chromatin-remodeling
	complexes play distinct roles during {A}rabidopsis development}},
  journal = {Plant Cell},
  year = {2005},
  volume = {17},
  pages = {2454--2472},
  number = {9},
  month = {Sep}
}

@ARTICLE{pmid12140326,
  author = {Sarnowski, Tomasz J and Swiezewski, Szymon and Pawlikowska, Katarzyna
	and Kaczanowski, Szymon and Jerzmanowski, Andrzej},
  title = {{{A}t{S}{W}{I}3{B}, an {A}rabidopsis homolog of {S}{W}{I}3, a core
	subunit of yeast {S}wi/{S}nf chromatin remodeling complex, interacts
	with {F}{C}{A}, a regulator of flowering time}},
  journal = {Nucleic Acids Res},
  year = {2002},
  volume = {30},
  pages = {3412--3421},
  number = {15},
  month = {Aug}
}

@ARTICLE{sarnowski02:_atswi3_arabid_swi3_swi_snf_fca,
  author = {Sarnowski, T.J., \'Swie\.zewski, S., Pawlikowska, K., Kaczanowski,
	S. and Jerzmanowski, A},
  title = {AtSWI3B, an Arabidopsis homolog of SWI3, a core subunit of yeast
	Swi/Snf chromatin remodeling complex, interacts with FCA, a regulator
	of flowering time.},
  journal = {Nucleic Acids Research},
  year = {2002},
  volume = {30},
  pages = {3412-3421}
}

@ARTICLE{segal03:module_nets,
  author = {Segal, E. and Shapira, M. and Regev, A. and Pe'er, D. and Botstein,
	D. and Koller, D. and Friedman, N.},
  title = {Module networks: identifying regulatory modules and their condition-specific
	regulators from gene expression data},
  journal = {Nat Genet},
  year = {2003},
  volume = {34},
  pages = {166-76},
  number = {2},
  abstract = {Much of a cell's activity is organized as a network of interacting
	modules: sets of genes coregulated to respond to different conditions.
	We present a probabilistic method for identifying regulatory modules
	from gene expression data. Our procedure identifies modules of coregulated
	genes, their regulators and the conditions under which regulation
	occurs, generating testable hypotheses in the form 'regulator X regulates
	module Y under conditions W'. We applied the method to a Saccharomyces
	cerevisiae expression data set, showing its ability to identify functionally
	coherent modules and their correct regulators. We present microarray
	experiments supporting three novel predictions, suggesting regulatory
	roles for previously uncharacterized proteins.}
}

@ARTICLE{segal01:rich_models,
  author = {Segal, E. and Taskar, B. and Gasch, A. and Friedman, N. and Koller,
	D.},
  title = {Rich probabilistic models for gene expression},
  journal = {Bioinformatics},
  year = {2001},
  volume = {17 Suppl 1},
  pages = {S243-52},
  abstract = {Clustering is commonly used for analyzing gene expression data. Despite
	their successes, clustering methods suffer from a number of limitations.
	First, these methods reveal similarities that exist over all of the
	measurements, while obscuring relationships that exist over only
	a subset of the data. Second, clustering methods cannot readily incorporate
	additional types of information, such as clinical data or known attributes
	of genes. To circumvent these shortcomings, we propose the use of
	a single coherent probabilistic model, that encompasses much of the
	rich structure in the genomic expression data, while incorporating
	additional information such as experiment type, putative binding
	sites, or functional information. We show how this model can be learned
	from the data, allowing us to discover patterns in the data and dependencies
	between the gene expression patterns and additional attributes. The
	learned model reveals context-specific relationships, that exist
	only over a subset of the experiments in the dataset. We demonstrate
	the power of our approach on synthetic data and on two real-world
	gene expression data sets for yeast. For example, we demonstrate
	a novel functionality that falls naturally out of our framework:
	predicting the "cluster" of the array resulting from a gene mutation
	based only on the gene's expression pattern in the context of other
	mutations.},
  keywords = {Algorithms Artificial Intelligence Cluster Analysis Computational
	Biology Databases, Genetic Gene Expression Profiling/*statistics
	& numerical data Genes, Fungal Genetic Techniques/statistics & numerical
	data *Models, Statistical Saccharomyces cerevisiae/genetics Support,
	Non-U.S. Gov't Support, U.S. Gov't, Non-P.H.S. Support, U.S. Gov't,
	P.H.S.}
}

@ARTICLE{segal03:em_protein_protein,
  author = {Segal, E. and Wang, H. and Koller, D.},
  title = {Discovering Molecular Pathways from Protein Interaction and Gene
	Expression Data},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19},
  number = {Suppl 1}
}

@ARTICLE{segal03:em_promoters,
  author = {Segal, E. and Yelensky, R. and Koller, D.},
  title = {Genome-wide Discovery of Transcriptional Modules from DNA Sequence
	and Gene Expression},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19 Suppl 1},
  pages = {273--282}
}

@ARTICLE{pmid11904377,
  author = {Shedden, Kerby and Cooper, Stephen},
  title = {{{A}nalysis of cell-cycle-specific gene expression in human cells
	as determined by microarrays and double-thymidine block synchronization}},
  journal = {Proc Natl Acad Sci U S A},
  year = {2002},
  volume = {99},
  pages = {4379--4384},
  number = {7},
  month = {Apr}
}

@ARTICLE{pmid17237067,
  author = {Shi, Y and Mitchell, T and Bar-Joseph, Z},
  title = {{{I}nferring {P}airwise {R}egulatory {R}elationships from {M}ultiple
	{T}ime {S}eries {D}atasets}},
  journal = {Bioinformatics},
  year = {2007},
  month = {Jan},
  note = {JOURNAL ARTICLE}
}

@ARTICLE{pmid11847074,
  author = {Shmulevich, Ilya and Dougherty, Edward R and Kim, Seungchan and Zhang,
	Wei},
  title = {{Probabilistic Boolean Networks: a rule-based uncertainty model for
	gene regulatory networks}},
  journal = {Bioinformatics},
  year = {2002},
  volume = {18},
  pages = {261--274},
  number = {2},
  month = {Feb}
}

@ARTICLE{Smith2006,
  author = {V. Anne Smith and Jing Yu and Tom V Smulders and Alexander J Hartemink
	and Erich D Jarvis},
  title = {Computational inference of neural information flow networks.},
  journal = {PLoS Comput Biol},
  year = {2006},
  volume = {2},
  pages = {e161},
  number = {11},
  month = {Nov},
  abstract = {Determining how information flows along anatomical brain pathways
	is a fundamental requirement for understanding how animals perceive
	their environments, learn, and behave. Attempts to reveal such neural
	information flow have been made using linear computational methods,
	but neural interactions are known to be nonlinear. Here, we demonstrate
	that a dynamic Bayesian network (DBN) inference algorithm we originally
	developed to infer nonlinear transcriptional regulatory networks
	from gene expression data collected with microarrays is also successful
	at inferring nonlinear neural information flow networks from electrophysiology
	data collected with microelectrode arrays. The inferred networks
	we recover from the songbird auditory pathway are correctly restricted
	to a subset of known anatomical paths, are consistent with timing
	of the system, and reveal both the importance of reciprocal feedback
	in auditory processing and greater information flow to higher-order
	auditory areas when birds hear natural as opposed to synthetic sounds.
	A linear method applied to the same data incorrectly produces networks
	with information flow to non-neural tissue and over paths known not
	to exist. To our knowledge, this study represents the first biologically
	validated demonstration of an algorithm to successfully infer neural
	information flow networks.},
  doi = {10.1371/journal.pcbi.0020161},
  keywords = {Action Potentials; Animals; Auditory Cortex; Auditory Perception;
	Computer Simulation; Electroencephalography; Evoked Potentials, Auditory;
	Finches; Information Theory; Models, Neurological; Nerve Net; Synaptic
	Transmission},
  owner = {bartek},
  pii = {06-PLCB-RA-0375R2},
  pmid = {17121460},
  timestamp = {2008.02.18},
  url = {http://dx.doi.org/10.1371/journal.pcbi.0020161}
}

@ARTICLE{snoussi89:_qualit,
  author = {El Houssine Snoussi},
  title = {Qualitative dynamics of piecewise-linear differential equations:
	a discrete mapping approach.},
  journal = {Dynamics and stability of systems},
  year = {1989},
  volume = {4},
  pages = {189-207},
  number = {3-4}
}

@ARTICLE{snoussi93:steadyStates,
  author = {El Houssine Snoussi and Rene Thomas},
  title = {Logical identification of all steady states: the concept of feedback
	loop characteristic states.},
  journal = {Bulletin of Mathematical Biology},
  year = {1993},
  volume = {55},
  pages = {973-991},
  number = {5}
}

@ARTICLE{spellman98:cell_cycle_genes,
  author = {Paul T. Spellman and Gavin Sherlock and Michael Q. Zhang and Vishwanath
	R. Iyer and Kirk Anders and Michael B. Eisen and Patrick O. Brown
	and David Botstein and Bruce Futcher},
  title = {Comprehensive Identification of Cell Cycle regulated Genes of the
	Yeast Saccharomyces cerevisiae by Microarray Hybridization},
  journal = {Molecular Biology of the Cell },
  year = {1998},
  volume = {9},
  pages = {3273--3297},
  number = {12},
  month = {Dec}
}

@ARTICLE{strahl00,
  author = {Strahl, B.D. and Allis, C.D.},
  title = {The language of covalent histone modifications.},
  journal = {Nature},
  year = {2000},
  volume = {403},
  pages = {41-45}
}

@ARTICLE{Tamada2003,
  author = {Yoshinori Tamada and SunYong Kim and Hideo Bannai and Seiya Imoto
	and Kousuke Tashiro and Satoru Kuhara and Satoru Miyano},
  title = {Estimating gene networks from gene expression data by combining Bayesian
	network model with promoter element detection.},
  journal = {Bioinformatics},
  year = {2003},
  volume = {19 Suppl 2},
  pages = {ii227--ii236},
  month = {Oct},
  abstract = {We present a statistical method for estimating gene networks and detecting
	promoter elements simultaneously. When estimating a network from
	gene expression data alone, a common problem is that the number of
	microarrays is limited compared to the number of variables in the
	network model, making accurate estimation a difficult task. Our method
	overcomes this problem by integrating the microarray gene expression
	data and the DNA sequence information into a Bayesian network model.
	The basic idea of our method is that, if a parent gene is a transcription
	factor, its children may share a consensus motif in their promoter
	regions of the DNA sequences. Our method detects consensus motifs
	based on the structure of the estimated network, then re-estimates
	the network using the result of the motif detection. We continue
	this iteration until the network becomes stable. To show the effectiveness
	of our method, we conducted Monte Carlo simulations and applied our
	method to Saccharomyces cerevisiae data as a real application.},
  keywords = {Bayes Theorem; Computer Simulation; Data Interpretation, Statistical;
	Gene Expression; Gene Expression Profiling; Models, Biological; Promoter
	Regions (Genetics); Proteome; Sequence Analysis, DNA; Signal Transduction},
  owner = {norbert},
  pmid = {14534194},
  timestamp = {2008.04.28}
}

@ARTICLE{tegner03,
  author = {Tegner, Jesper and Yeung, M K Stephen and Hasty, Jeff and Collins,
	James J},
  title = {{Reverse engineering gene networks: integrating genetic perturbations
	with dynamical modeling}},
  journal = {Proc Natl Acad Sci U S A},
  year = {2003},
  volume = {100},
  pages = {5944--5949},
  number = {10},
  month = {May}
}

@ARTICLE{pmid12888004,
  author = {Thieffry, Denis and Sanchez, Lucas},
  title = {{Dynamical modelling of pattern formation during embryonic development}},
  journal = {Current Opinions on Genetic Development},
  year = {2003},
  volume = {13},
  pages = {326--330},
  number = {4},
  month = {Aug}
}

@ARTICLE{pmid12547678,
  author = {Thieffry, Denis and Sanchez, Lucas},
  title = {{Alternative epigenetic states understood in terms of specific regulatory
	structures}},
  journal = {Ann N Y Acad Sci},
  year = {2002},
  volume = {981},
  pages = {135--153},
  month = {Dec}
}

@ARTICLE{Thomas73,
  author = {Rene Thomas},
  title = {Boolean formalization of genetic control circuits},
  journal = {Journal of Theoretical Biology},
  year = {1973},
  volume = {42},
  pages = {563}
}

@BOOK{thomas90:_biolog_feedb,
  title = {Biological Feedback},
  publisher = {CRC Press},
  year = {1990},
  author = {Rene Thomas and Richard D'Ari.}
}

@ARTICLE{thomas01:_multis,
  author = {R. Thomas and M. Kaufman},
  title = {Multistationarity, the basis of cell differentiation and memory.
	II. Logical analysis of regulatory networks in terms of feedback
	circuits},
  journal = {Chaos},
  year = {2001}
}

@ARTICLE{thomas2001mbc,
  author = {Thomas, R. and Kaufman, M.},
  title = {{Multistationarity, the basis of cell differentiation and memory.
	I. Structural conditions of multistationarity and other nontrivial
	behavior}},
  journal = {Chaos: An Interdisciplinary journal of Nonlinear Science},
  year = {2001},
  volume = {11},
  pages = {170},
  publisher = {AIP}
}

@ARTICLE{pmid15637633,
  author = {Tompa, Martin and Li, Nan and Bailey, Timothy L and Church, George
	M and De Moor, Bart and Eskin, Eleazar and Favorov, Alexander V and
	Frith, Martin C and Fu, Yutao and Kent, W James and Makeev, Vsevolod
	J and Mironov, Andrei A and Noble, William Stafford and Pavesi, Giulio
	and Pesole, Graziano and Regnier, Mireille and Simonis, Nicolas and
	Sinha, Saurabh and Thijs, Gert and van Helden, Jacques and Vandenbogaert,
	Mathias and Weng, Zhiping and Workman, Christopher and Ye, Chun and
	Zhu, Zhou},
  title = {{Assessing computational tools for the discovery of transcription
	factor binding sites}},
  journal = {Nat Biotechnol},
  year = {2005},
  volume = {23},
  pages = {137--144},
  number = {1},
  month = {Jan}
}

@BOOK{tsonis,
  title = {Anatomy of gene regulation},
  publisher = {Garland Publishing},
  year = {2003},
  author = {Tsonis, P.}
}

@ARTICLE{vilo,
  author = {Vilo, J. and Brazma, A. and Jonassen, I. and Robinson, A. and Ukkonen,
	E.},
  title = {Mining for putative regulatory elements in the yeast genome using
	gene expression data},
  journal = {Proc Int Conf Intell Syst Mol Biol},
  year = {2000},
  pages = {384-94.},
  number = {8}
}

@ARTICLE{Vinh2011,
  author = {Nguyen Xuan Vinh and Madhu Chetty and Ross Coppel and Pramod P Wangikar},
  title = {GlobalMIT: learning globally optimal dynamic bayesian network with
	the mutual information test criterion.},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {2765--2766},
  number = {19},
  month = {Oct},
  __markedentry = {[norbert:6]},
  abstract = {Dynamic Bayesian networks (DBN) are widely applied in modeling various
	biological networks including the gene regulatory network (GRN).
	Due to the NP-hard nature of learning static Bayesian network structure,
	most methods for learning DBN also employ either local search such
	as hill climbing, or a meta stochastic global optimization framework
	such as genetic algorithm or simulated annealing.This article presents
	GlobalMIT, a toolbox for learning the globally optimal DBN structure
	from gene expression data. We propose using a recently introduced
	information theoretic-based scoring metric named mutual information
	test (MIT). With MIT, the task of learning the globally optimal DBN
	is efficiently achieved in polynomial time.The toolbox, implemented
	in Matlab and C++, is available at http://code.google.com/p/globalmit.vinh.nguyen@monash.edu;
	madhu.chetty@monash.eduSupplementary data is available at Bioinformatics
	online.},
  doi = {10.1093/bioinformatics/btr457},
  institution = {Gippsland School of Information Technology, Faculty of IT, Monash
	University, Victoria, Australia. vinh.nguyen@monash.edu},
  keywords = {Algorithms; Bayes Theorem; Gene Expression; Gene Expression Regulation;
	Gene Regulatory Networks; Information Storage and Retrieval; Metabolic
	Networks and Pathways; Models, Biological; Oligonucleotide Array
	Sequence Analysis, methods; Software},
  language = {eng},
  medline-pst = {ppublish},
  owner = {norbert},
  pii = {btr457},
  pmid = {21813478},
  timestamp = {2012.09.07},
  url = {http://dx.doi.org/10.1093/bioinformatics/btr457}
}

@ARTICLE{wagner99:cooperative,
  author = {Wagner, A.},
  title = {Genes regulated cooperatively by one or more transcription factors
	and their identification in whole eukaryotic genomes},
  journal = {Bioinformatics},
  year = {1999},
  volume = {15},
  pages = {776-84},
  number = {10},
  abstract = {MOTIVATION: The question addressed here is how cooperative interactions
	among transcription factors (TFs), a very frequent phenomenon in
	eukaryotic transcriptional regulation, can be used to identify genes
	that are regulated by one or more TFs with known DNA binding specificities.
	Cooperativity may be homotypic, involving binding of only one transcription
	factor to multiple sites in a gene's regulatory region. It may also
	be heterotypic, involving binding of more than one TF. Both types
	of cooperativity have in common that the binding sites for the respective
	TFs form tightly linked 'clusters', groups of binding sites often
	more closely associated than expected by chance alone. RESULTS: A
	statistical technique suitable for the identification of statistically
	significant homotypic or heterotypic TF binding site clusters in
	whole eukaryotic genomes is presented. It can be used to identify
	genes likely to be regulated by the TFs. Application of the technique
	is illustrated with two transcription factors involved in the cell
	cycle and mating control of the yeast Saccharomyces cerevisiae, indicating
	that the results obtained are biologically meaningful. This rapid
	and inexpensive computational method of generating hypotheses about
	gene regulation thus generates information that may be used to guide
	subsequent costly and laborious experimental approaches, and that
	may aid in the assignment of biological functions to putative open
	reading frames.},
  keywords = {Base Sequence Binding Sites/genetics *Computational Biology DNA, Fungal/genetics/metabolism
	Eukaryotic Cells *Gene Expression Regulation Genome Genome, Fungal
	Saccharomyces cerevisiae/genetics/metabolism Support, Non-U.S. Gov't
	Transcription Factors/*metabolism}
}

@ARTICLE{pmid12058064,
  author = {Whitfield, Michael L and Sherlock, Gavin and Saldanha, Alok J and
	Murray, John I and Ball, Catherine A and Alexander, Karen E and Matese,
	John C and Perou, Charles M and Hurt, Myra M and Brown, Patrick O
	and Botstein, David},
  title = {{{I}dentification of genes periodically expressed in the human cell
	cycle and their expression in tumors}},
  journal = {Mol Biol Cell},
  year = {2002},
  volume = {13},
  pages = {1977--2000},
  number = {6},
  month = {Jun}
}

@ARTICLE{wilczynski06:sln,
  author = {Wilczy\'nski, Bartek},
  title = {A stochastic extension of \{{R}\}.thomas regulatory network modelling},
  journal = {Stochastic models in biological sciences, Workshop - Warsaw, Poland,
	29 May - 2 June 2006, proceedings to appear in Banach Center Publications},
  year = {2006}
}

@ARTICLE{wilczynski05:gln_from_data,
  author = {Wilczy\'nski, Bartek},
  title = {Generalized Logical Network Reconstruction from Data.},
  journal = {Workshop on Recent Trends in Applications of Mathematics to Biology
	and Medicine II},
  year = {2005}
}

@ARTICLE{Wilczynski:_using_local_gene_expres_simil,
  author = {Wilczy\'nski, Bartek and Hvidsten, Torgeir R. and Kryshtafovych,
	A. and Tiuryn, J. and Komorowski, J. and Fidelis, K.},
  title = {Using Local Gene Expression Similarities to Discover Regulatory Binding
	Site Modules},
  journal = {BMC Bioinformatics},
  year = {2006},
  volume = {7},
  pages = {505}
}

@ARTICLE{06:recon_sln,
  author = {Wilczy\'nski, Bartek and Tiuryn, Jerzy},
  title = {Regulatory Network Reconstruction Using {S}tochastic {L}ogical {N}etworks},
  journal = {Lecture Notes in Computer Science},
  year = {2006},
  volume = {4210},
  pages = {142-154},
  note = {http://dx.doi.org/10.1007/11885191\_10}
}

@ARTICLE{pmid10902194,
  author = {Workman, C T and Stormo, G D},
  title = {{ANN-Spec: a method for discovering transcription factor binding
	sites with improved specificity}},
  journal = {Pac Symp Biocomput},
  year = {2000},
  pages = {467--478}
}

@ARTICLE{workman98:_alter,
  author = {Workman, J.L., and Kingston, R.E.},
  title = {Alteration of nucleosome structure as a mechanism of transcriptional
	regulation.},
  journal = {Annu. Rev. Biochem.},
  year = {1998},
  volume = {67},
  number = {545-579}
}

@ARTICLE{Xuan2012,
  author = {Nguyen Xuan and Madhu Chetty and Ross Coppel and Pramod P Wangikar},
  title = {Gene regulatory network modeling via global optimization of high-order
	dynamic Bayesian network.},
  journal = {BMC Bioinformatics},
  year = {2012},
  volume = {13},
  pages = {131},
  number = {1},
  __markedentry = {[norbert:6]},
  abstract = {ABSTRACT: BACKGROUND: Dynamic Bayesian network (DBN) is among the
	mainstream approaches for modeling various biological networks, including
	the gene regulatory network (GRN). Most current methods for learning
	DBN employ either local search such as hill-climbing, or a meta stochastic
	global optimization framework such as genetic algorithm or simulated
	annealing, which are only able to locate sub-optimal solutions. Further,
	current DBN applications have essentially been limited to small sized
	networks. RESULTS: To overcome the above difficulties, we introduce
	here a deterministic global optimization based DBN approach for reverse
	engineering genetic networks from time course gene expression data.
	For such DBN models that consist only of inter time slice arcs, we
	show that there exists a polynomial time algorithm for learning the
	globally optimal network structure. The proposed approach, named
	GlobalMIT+, employs the recently proposed information theoretic scoring
	metric named mutual information test (MIT). GlobalMIT+ is able to
	learn high-order time delayed genetic interactions, which are common
	to most biological systems. Evaluation of the approach using both
	synthetic and real data sets, including a 733 cyanobacterial gene
	expression data set, shows significantly improved performance over
	other techniques. CONCLUSIONS: Our studies demonstrate that deterministic
	global optimization approaches can infer large scale genetic networks.},
  doi = {10.1186/1471-2105-13-131},
  institution = {Gippsland School of Information Technology, Monash University, Melbourne,
	Australia. vinh.nguyen@monash.edu.},
  language = {eng},
  medline-pst = {epublish},
  owner = {norbert},
  pii = {1471-2105-13-131},
  pmid = {22694481},
  timestamp = {2012.09.07},
  url = {http://dx.doi.org/10.1186/1471-2105-13-131}
}

@INPROCEEDINGS{yoo02,
  author = {C. Yoo and V. Thorsson and G.F. Cooper},
  title = {Discovery Of Causal Relationships In A Gene Regulation Pathway From
	A Mixture Of Experimental and Oberservational {DNA} Microarray Data},
  booktitle = {Proceedings of Pacific Symposium on Biocomputing 7:498-509},
  year = {2002}
}

@INPROCEEDINGS{zak01,
  author = {Zak, Daniel E and Doyle, Francis J 3rd and Gonye, Gregory E and Schwaber,
	James S},
  title = {{Simulation Studies for the Identification of Genetic Networks from
	cDNA Array and Regulatory Activity Data}},
  booktitle = {Proceedings of the Second International Conference on Systems Biology},
  year = {2001},
  pages = {231--238}
}

@ARTICLE{zak03,
  author = {Zak, Daniel E and Gonye, Gregory E and Schwaber, James S and Doyle,
	Francis J 3rd},
  title = {{Importance of input perturbations and stochastic gene expression
	in the reverse engineering of genetic regulatory networks: insights
	from an identifiability analysis of an in silico network}},
  journal = {Genome Res.},
  year = {2003},
  volume = {13},
  pages = {2396--2405},
  number = {11},
  month = {Nov}
}

@ARTICLE{pmid16845143,
  author = {Zhao, Wentao and Serpedin, Erchin and Dougherty, Edward R},
  title = {{{I}nferring gene regulatory networks from time series data using
	the minimum description length principle}},
  journal = {Bioinformatics},
  year = {2006},
  volume = {22},
  pages = {2129--2135},
  number = {17},
  month = {Sep}
}

@ARTICLE{zhu99:scpd,
  author = {Zhu, J. and Zhang, M. Q.},
  title = {SCPD: a promoter database of the yeast Saccharomyces cerevisiae},
  journal = {Bioinformatics},
  year = {1999},
  volume = {15},
  pages = {607-11},
  number = {7-8},
  abstract = {MOTIVATION: In order to facilitate a systematic study of the promoters
	and transcriptionally regulatory cis-elements of the yeast Saccharomyces
	cerevisiae on a genomic scale, we have developed a comprehensive
	yeast-specific promoter database, SCPD. RESULTS: Currently SCPD contains
	580 experimentally mapped transcription factor (TF) binding sites
	and 425 transcriptional start sites (TSS) as its primary data entries.
	It also contains relevant binding affinity and expression data where
	available. In addition to mechanisms for promoter information (including
	sequence) retrieval and a data submission form, SCPD also provides
	some simple but useful tools for promoter sequence analysis. AVAILABILITY:
	SCPD can be accessed from the URL http://cgsigma.cshl.org/jian. The
	database is continually updated.},
  keywords = {Binding Sites/genetics *Databases, Factual Genes, Fungal Genes, Regulator
	Genome, Fungal *Promoter Regions (Genetics) Saccharomyces cerevisiae/*genetics/metabolism
	Support, U.S. Gov't, P.H.S. TATA Box Transcription Factors/metabolism}
}

@ARTICLE{zhu02:tfcc,
  author = {Zhu, Z. and Pilpel, Y. and Church, G. M.},
  title = {Computational identification of transcription factor binding sites
	via a transcription-factor-centric clustering (TFCC) algorithm},
  journal = {J Mol Biol},
  year = {2002},
  volume = {318},
  pages = {71-81},
  number = {1},
  abstract = {While microarray-based expression profiling has facilitated the use
	of computational methods to find potential cis-regulatory promoter
	elements, few current in silico approaches explicitly link regulatory
	motifs with the transcription factors that bind them. We have thus
	developed a TF-centric clustering (TFCC) algorithm that may provide
	such missing information through incorporation of biological knowledge
	about TFs. TFCC is a semi-supervised clustering algorithm which relies
	on the assumption that the expression profiles of some TFs may be
	related to those of the genes under their control. We examined this
	premise and found the vicinities of TFs in expression space are often
	enriched with the genes they regulate. So, instead of clustering
	genes based on the mutual similarity of their expression profiles
	to each other, we used TFs as seeds to group together genes whose
	expression patterns correlate with that of a particular TF. Then
	a Gibbs sampling algorithm was applied to search for shared cis-regulatory
	elements in promoters of clustered genes. Our working hypothesis
	was that if a TF-centric cluster indeed contains many targets of
	the seeding TF, at least one of the discovered motifs would be the
	site bound by the very same TF. We tested the TFCC approach on eight
	cell cycle and sporulation regulating TFs whose binding sites have
	been previously characterized in Saccharomyces cerevisiae, and correctly
	identified binding site motifs for half of them. In addition, we
	also made de novo predictions for some unknown TF binding sites.},
  keywords = {Algorithms Binding Sites Cell Cycle Computational Biology/*methods
	Databases, Factual Gene Expression Profiling/methods Open Reading
	Frames Predictive Value of Tests Promoter Regions (Genetics) Saccharomyces
	cerevisiae/genetics/metabolism Support, Non-U.S. Gov't Support, U.S.
	Gov't, Non-P.H.S. Transcription Factors/genetics/*metabolism}
}

@ARTICLE{zou05,
  author = {Zou, Min and Conzen, Suzanne D},
  title = {{A new dynamic Bayesian network (DBN) approach for identifying gene
	regulatory networks from time course microarray data}},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  pages = {71--79},
  number = {1},
  month = {Jan}
}

@OTHER{GO_webpage,
  title = {Gene Ontology website},
  url = {http://www.geneontology.org}
}

@MISC{supapplying,
  title = { Applying dynamic Bayesian networks to perturbed gene expression
	data -- web supplement},
  url = {http://bioputer.mimuw.edu.pl/papers/pert_expr/}
}

@OTHER{supp_webpage,
  title = {Yeast Cell Cycle supplementary webpage},
  url = {http://bioputer.mimuw.edu.pl/~bartek/CellCycle/}
}
@ARTICLE{globMIT,
  author = {Nguyen Xuan Vinh and  Madhu Chetty and  Ross Coppel and Pramod P.Wangikar},
  title = {GlobalMIT: learning globally optimal dynamic bayesian network
with the mutual information test criterion
},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {2765–2766},
  number = {19}
}

@ARTICLE{deCampos,
  author = {Luis M. de Campos},
  title = {A Scoring Function for Learning Bayesian Networks based on Mutual
Information and Conditional Independence Tests},
  journal = {Journal of Machine Learning Research},
  year = {2006},
  pages = {2149-2187},
  number = {7}
}

@ARTICLE{globMITManual,
  author = {Vinh Nguyen},
  title = {The GlobalMIT Toolkit for Learning Optimal Dynamic Bayesian Network User Guide },
  year = {2011},
}
@article{bnfinder,
  title={BNFinder: exact and efficient method for learning Bayesian networks},
  author={Wilczy{\'n}ski, B. and Dojer, N.},
  journal={Bioinformatics},
  volume={25},
  number={2},
  pages={286},
  year={2009},
  publisher={Oxford Univ Press}
}
