JabRef References output

Author	Title	Year	Journal/Proceedings	Reftype	DOI/URL
Claudia Rangel, John Angus, Z. G. D. L. W. (c. 9).	Probabilistic Modeling in Bioinformatics and Medical Informatics [BibTeX]	2005		book
BibTeX: @book{husmeier, author = {Claudia Rangel, John Angus, Zoubin Ghahramani, David L. Wild (ch 9)}, title = {Probabilistic Modeling in Bioinformatics and Medical Informatics}, publisher = {Springer}, year = {2005} }
Friedman, N.	Inferring Cellular Networks Using Probablistic Graphical Models [BibTeX]	2004	science	article	URL
BibTeX: @article{firedman, author = {N. Friedman}, title = {Inferring Cellular Networks Using Probablistic Graphical Models}, journal = {science}, year = {2004}, volume = {303}, url = {http://www.sciencemag.org} }
J. Liao, R. Boscolo, Y. Y. L. M. T. C. S. & Roychowdhury, V. P.	Networks component analysis: Reconstruction of regulatory signals in biological systems [Abstract] [BibTeX]	2003	PNAS	article	URL
Abstract: High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.
BibTeX: @article{liao, author = {J. Liao, R. Boscolo, Y.-L. Yang, L. M. Tran, C. Sabatti, and V. P. Roychowdhury}, title = {Networks component analysis: Reconstruction of regulatory signals in biological systems}, journal = {PNAS}, year = {2003}, volume = {100}, number = {26}, pages = {15522-15527}, url = {http://www.pnas.org/cgi/doi/10.1073/pnas.2136632100} }
Markowetz, F. & Spang, R.	Inferring Cellular Networks - A Review [Abstract] [BibTeX]	2007	BMC Bioinformatics	article	DOI URL
Abstract: In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.
BibTeX: @article{markwetz, author = {F. Markowetz and R. Spang}, title = {Inferring Cellular Networks - A Review}, journal = {BMC Bioinformatics}, year = {2007}, volume = {8}, number = {6}, url = {http://www.biomedcentral.com/1471-2105/8/s6/s5}, doi = {http://dx.doi.org/10.1186/1471-2105-8-s6-s5} }
N. Friedman, M. Linial, I. N. & Pe'er, D.	Using Bayesian Networks to Analyze Expression Data [BibTeX]	2000		proceedings	URL
BibTeX: @proceedings{friedman2, author = {N. Friedman, M. Linial, I. Nachman, and D. Pe'er}, title = {Using Bayesian Networks to Analyze Expression Data}, year = {2000}, url = {www.sysbio.harvard.edu/csb/ramanathan_lab/iftach/papers/FLNP1Full.pdf} }
Opgen-Rhein, R. & Strimmer, K.	From Correlation to Causation Networks: A Simple Approximate Learning Algorithm and Its Application to High-Dimensional Plant Gene Expression Data [BibTeX]	2007	BMC Systems Biology	article	DOI URL
BibTeX: @article{Opgen-Rhein2007, author = {Rainer Opgen-Rhein and Korbinian Strimmer}, title = {From Correlation to Causation Networks: A Simple Approximate Learning Algorithm and Its Application to High-Dimensional Plant Gene Expression Data}, journal = {BMC Systems Biology}, year = {2007}, pages = {1-37}, url = {http://www.biomedcentral.com/1752-0509/1/37}, doi = {http://dx.doi.org/10.1186/1752-0509-1-37} }
Pearl, J.	Causality [BibTeX]	2000		book
BibTeX: @book{pearl, author = {J. Pearl}, title = {Causality}, publisher = {Cambridge University Press}, year = {2000} }
S. Y. Kim, S. I. & Miyano, S.	Inferring Gene Networks From Time Series Microarray Data Using Dynamic Bayesian Networks [Abstract] [BibTeX]	2003	Briefings in Bioinformatics	article	URL
Abstract: Dynamic Bayesian networks (DBNs) are considered as a promising model for inferring gene networks from time series microarray data. DBNs have overtaken Bayesian networks (BNs) as DBNs can construct cyclic regulations using time delay information. In this paper, a general framework for DBN modelling is outlined. Both discrete and continuous DBN models are constructed systematically and criteria for learning network structures are introduced from a Bayesian statistical viewpoint. This paper reviews the applications of DBNs over the past years. Real data applications for Saccharomyces cerevisiae time series gene expression data are also shown. Keywords: microarray, gene networks, DBNs
BibTeX: @article{kim, author = {S. Y. Kim, S. Imoto, and S. Miyano}, title = {Inferring Gene Networks From Time Series Microarray Data Using Dynamic Bayesian Networks}, journal = {Briefings in Bioinformatics}, year = {2003}, volume = {4}, number = {3}, pages = {228-235}, url = {http://bib.oxfordjournals.org/cgi/content/abstract/4/3/228} }
Whittaker, J.	Graphical Models in Applied Mathematical Multivariate Statistics [BibTeX]	1990		book
BibTeX: @book{whittaker, author = {J. Whittaker}, title = {Graphical Models in Applied Mathematical Multivariate Statistics}, publisher = {Wiley}, year = {1990} }
Zou, M. & Conzen, S. D.	A new dynamic Bayesian netowrk (DBN) approah for identifying gene regulatory networks from time course microarray data [Abstract] [BibTeX]	2005	Bioinformatics	article	DOI URL
Abstract: Motivation: Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian Network (DBN) is an important approach for predicting gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time. Results: In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up- or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time series expression data measured during the yeast cell cycle. Results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches. Availability: The programs described in this paper can be obtained from the corresponding author upon request.
BibTeX: @article{zou, author = {Min Zou and Suzanne D. Conzen}, title = {A new dynamic Bayesian netowrk (DBN) approah for identifying gene regulatory networks from time course microarray data}, journal = {Bioinformatics}, year = {2005}, volume = {21}, number = {1}, pages = {71-79}, url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/bth463v1}, doi = {http://dx.doi.org/10.1093/bioinformatics/bth463} }

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