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  • 1
    In: Handbook of Research on Computational Methodologies in Gene Regulatory Networks, Chapter 6, pp.139-168
    Description: Differential equation models provide a detailed, quantitative description of transcription regulatory networks. However, due to the large number of model parameters, they are usually applicable to small networks only, with at most a few dozen genes. Moreover, they are not well suited to deal with noisy data. In this chapter, we show how to circumvent these limitations by integrating an ordinary differential equation model into a stochastic framework. The resulting model is then embedded into a Bayesian learning approach. We integrate the-biologically motivated-expectation of sparse connectivity in the network into the inference process using a specifically defined prior distribution on model parameters. The approach is evaluated on simulated data and a dataset of the transcriptional network governing the yeast cell cycle.
    ISBN: 9781605666853
    Source: IGI Global
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  • 2
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Bioinformatics Research and Development: First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007. Proceedings, pp.77-89
    Description: It has previously been demonstrated that gene expression data correlate with event-free and overall survival in several cancers. A number of methods exist that assign patients to different risk classes based on expression profiles of their tumor. However, predictions of actual survival times in years for the individual patient, together with confidence intervals on the predictions made, would provide a far more detailed view, and could aid the clinician considerably in evaluating different treatment options. Similarly, a method able to make such predictions could be analyzed to infer knowledge about the relevant disease genes, hinting at potential disease pathways and pointing to relevant targets for drug design. Here too, confidences on the relevance values for the individual genes would be useful to have.Our algorithm to tackle these questions builds on a hierarchical Bayesian approach, combining a Cox regression model with a hierarchical prior distribution on the regression parameters for feature selection. This prior enables the method to efficiently deal with the low sample number, high dimensionality setting characteristic of microarray datasets. We then sample from the posterior distribution over a patients survival time, given gene expression measurements and training data. This enables us to make statements such as ”with probability 0.6, the patient will survive between 3 and 4 years”. A similar approach is used to compute relevance values with confidence intervals for the individual genes measured.The method is evaluated on a simulated dataset, showing feasibility of the approach. We then apply the algorithm to a publicly available dataset on diffuse large B-cell lymphoma, a cancer of the lymphocytes, and demonstrate that it successfully predicts survival times and survival time distributions for the individual patient.
    Keywords: Computer Science ; Computational Biology/Bioinformatics ; Data Mining and Knowledge Discovery ; Artificial Intelligence (Incl. Robotics) ; Information Storage and Retrieval ; Probability and Statistics in Computer Science ; Computer Appl. in Life Sciences ; Biology ; Computer Science
    ISBN: 9783540712329
    ISBN: 3540712321
    Source: SpringerLink Books
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  • 3
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Bioinformatics Research and Development: First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007. Proceedings, pp.1-15
    Description: Differential equations have been established to model the dynamic behavior of gene regulatory networks in the last few years. They provide a detailed insight into regulatory processes at a molecular level. However, in a top down approach aiming at the inference of the underlying regulatory network from gene expression data, the corresponding optimization problem is usually severely underdetermined, since the number of unknowns far exceeds the number of timepoints available. Thus one has to restrict the search space in a biologically meaningful way.We use differential equations to model gene regulatory networks and introduce a Bayesian regularized inference method that is particularly suited to deal with sparse and noisy datasets. Network inference is carried out by embedding our model into a probabilistic framework and maximizing the posterior probability. A specifically designed hierarchical prior distribution over interaction strenghts favours sparse networks, enabling the method to efficiently deal with small datasets.Results on a simulated dataset show that our method correctly learns network structure and model parameters even for short time series. Furthermore, we are able to learn main regulatory interactions in the yeast cell cycle.
    Keywords: Computer Science ; Computational Biology/Bioinformatics ; Data Mining and Knowledge Discovery ; Artificial Intelligence (Incl. Robotics) ; Information Storage and Retrieval ; Probability and Statistics in Computer Science ; Computer Appl. in Life Sciences ; Biology ; Computer Science
    ISBN: 9783540712329
    ISBN: 3540712321
    Source: SpringerLink Books
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  • 4
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Studies in Computational Intelligence, Computational Intelligence in Bioinformatics, pp.33-74
    Description: Gene regulatory networks describe how cells control the expression of genes, which, together with some additional regulation further downstream, determines the production of proteins essential for cellular function. The level of expression of each gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated to protein. RNA and protein expression will influence expression rates of other genes, thus giving rise to a complicated network structure.An analysis of regulatory processes within the cell will significantly further our understanding of cellular dynamics. It will shed light on normal and abnormal, diseased cellular events, and may provide information on pathways in dire diseases such as cancer. These pathways can provide information on how the disease develops, and what processes are involved in progression. Ultimately, we can hope that this will provide us with new therapeutic approaches and targets for drug design.It is thus no surprise that many efforts have been undertaken to reconstruct gene regulatory networks from gene expression measurements. In this chapter, we will provide an introductory overview over the field. In particular, we will present several different approaches to gene regulatory network inference, discuss their strengths and weaknesses, and provide guidelines on which models are appropriate under what circumstances. In addition, we sketch future developments and open problems.
    Keywords: Engineering ; Appl.Mathematics/Computational Methods of Engineering ; Artificial Intelligence (Incl. Robotics) ; Bioinformatics ; Engineering ; Applied Sciences ; Biology
    ISBN: 9783540768029
    ISBN: 3540768025
    Source: SpringerLink Books
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