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@ARTICLE{Gao2008,
  author = {Pei Gao and Antti Honkela and Magnus Rattray and Neil D Lawrence},
  title = {Gaussian process modelling of latent chemical species: applications
	to inferring transcription factor activities.},
  journal = {Bioinformatics},
  year = {2008},
  volume = {24},
  pages = {i70--i75},
  number = {16},
  month = {Aug},
  abstract = {MOTIVATION: Inference of latent chemical species in biochemical interaction
	networks is a key problem in estimation of the structure and parameters
	of the genetic, metabolic and protein interaction networks that underpin
	all biological processes. We present a framework for Bayesian marginalization
	of these latent chemical species through Gaussian process priors.
	RESULTS: We demonstrate our general approach on three different biological
	examples of single input motifs, including both activation and repression
	of transcription. We focus in particular on the problem of inferring
	transcription factor activity when the concentration of active protein
	cannot easily be measured. We show how the uncertainty in the inferred
	transcription factor activity can be integrated out in order to derive
	a likelihood function that can be used for the estimation of regulatory
	model parameters. An advantage of our approach is that we avoid the
	use of a coarsegrained discretization of continuous time functions,
	which would lead to a large number of additional parameters to be
	estimated. We develop exact (for linear regulation) and approximate
	(for non-linear regulation) inference schemes, which are much more
	efficient than competing sampling-based schemes and therefore provide
	us with a practical toolkit for model-based inference. AVAILABILITY:
	The software and data for recreating all the experiments in this
	paper is available in MATLAB from http://www.cs.man. ac.uk/~neill/gpsim.},
  doi = {10.1093/bioinformatics/btn278},
  institution = {School of Computer Science, University of Manchester, Kilburn Building,
	Oxford Road, Manchester.},
  keywords = {Computer Simulation; Models, Chemical; Models, Genetic; Models, Statistical;
	Normal Distribution; RNA, Messenger, chemistry/genetics; Transcription
	Factors, chemistry/genetics; Transcriptional Activation, genetics},
  language = {eng},
  medline-pst = {ppublish},
  owner = {magnus},
  pii = {btn278},
  pmid = {18689843},
  timestamp = {2009.08.20},
  url = {http://dx.doi.org/10.1093/bioinformatics/btn278}
}

@ARTICLE{Honkela2011,
  author = {Antti Honkela and Pei Gao and Jonatan Ropponen and Magnus Rattray
	and Neil D Lawrence},
  title = {tigre: Transcription factor inference through gaussian process reconstruction
	of expression for bioconductor.},
  journal = {Bioinformatics},
  year = {2011},
  volume = {27},
  pages = {1026--1027},
  number = {7},
  month = {Apr},
  abstract = {tigre is an R/Bioconductor package for inference of transcription
	factor activity and ranking candidate target genes from gene expression
	time series. The underlying methodology is based on Gaussian process
	inference on a differential equation model that allows the use of
	short, unevenly sampled, time series. The method has been designed
	with efficient parallel implementation in mind, and the package supports
	parallel operation even without additional software. AVAILABILITY:
	The tigre package is included in Bioconductor since release 2.6 for
	R 2.11. The package and a user's guide are available at http://www.bioconductor.org.},
  doi = {10.1093/bioinformatics/btr057},
  institution = {Helsinki Institute for Information Technology HIIT, University of
	Helsinki, Helsinki, Finland. antti.honkela@hiit.fi},
  keywords = {Gene Expression Profiling; Gene Expression Regulation; Oligonucleotide
	Array Sequence Analysis; Software; Transcription Factors, metabolism},
  language = {eng},
  medline-pst = {ppublish},
  owner = {ahonkela},
  pii = {btr057},
  pmid = {21300702},
  timestamp = {2011.10.17},
  url = {http://dx.doi.org/10.1093/bioinformatics/btr057}
}

@ARTICLE{Honkela2010PNAS,
  author = {Antti Honkela and Charles Girardot and E. Hilary Gustafson and Ya-Hsin
	Liu and Eileen E M Furlong and Neil D Lawrence and Magnus Rattray},
  title = {Model-based method for transcription factor target identification
	with limited data.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2010},
  volume = {107},
  pages = {7793--7798},
  number = {17},
  month = {Apr},
  abstract = {We present a computational method for identifying potential targets
	of a transcription factor (TF) using wild-type gene expression time
	series data. For each putative target gene we fit a simple differential
	equation model of transcriptional regulation, and the model likelihood
	serves as a score to rank targets. The expression profile of the
	TF is modeled as a sample from a Gaussian process prior distribution
	that is integrated out using a nonparametric Bayesian procedure.
	This results in a parsimonious model with relatively few parameters
	that can be applied to short time series datasets without noticeable
	overfitting. We assess our method using genome-wide chromatin immunoprecipitation
	(ChIP-chip) and loss-of-function mutant expression data for two TFs,
	Twist, and Mef2, controlling mesoderm development in Drosophila.
	Lists of top-ranked genes identified by our method are significantly
	enriched for genes close to bound regions identified in the ChIP-chip
	data and for genes that are differentially expressed in loss-of-function
	mutants. Targets of Twist display diverse expression profiles, and
	in this case a model-based approach performs significantly better
	than scoring based on correlation with TF expression. Our approach
	is found to be comparable or superior to ranking based on mutant
	differential expression scores. Also, we show how integrating complementary
	wild-type spatial expression data can further improve target ranking
	performance.},
  doi = {10.1073/pnas.0914285107},
  institution = {Department of Information and Computer Science, Aalto University
	School of Science and Technology, Helsinki, Finland. antti.honkela@tkk.fi},
  keywords = {Bayes Theorem; Chromatin Immunoprecipitation; Drosophila Proteins,
	metabolism; Gene Expression Regulation, genetics/physiology; Gene
	Regulatory Networks, genetics; Likelihood Functions; Models, Genetic;
	Mutation, genetics; Myogenic Regulatory Factors, metabolism; Systems
	Biology, methods; Twist Transcription Factor, metabolism},
  language = {eng},
  medline-pst = {ppublish},
  owner = {ahonkela},
  pii = {0914285107},
  pmid = {20385836},
  timestamp = {2011.10.17},
  url = {http://dx.doi.org/10.1073/pnas.0914285107}
}

@INCOLLECTION{Lawrence2007,
  author = {Neil D. Lawrence and Guido Sanguinetti and Magnus Rattray},
  title = {Modelling transcriptional regulation using {G}aussian processes},
  booktitle = {Advances in Neural Information Processing Systems},
  publisher = {MIT Press},
  year = {2007},
  editor = {B. Sch\"{o}lkopf and J. C. Platt and T. Hofmann},
  volume = {19},
  pages = {785-792},
  address = {Cambridge, MA},
  owner = {ahonkela},
  timestamp = {2009.09.22}
}

