Inferring Gene Dependency Networks from Genomic Longitudinal Data: a Functional Data Approach
نویسندگان
چکیده
• A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell. Here we investigate a graphical model that treats the observed gene expression over time as realizations of random curves. This approach is centered around an estimator of dynamical pairwise correlation that takes account of the functional nature of the observed data. This allows to extend the graphical Gaussian modeling framework from i.i.d. data to analyze longitudinal genomic data. The new method is illustrated by analyzing highly replicated data from a genome experiment concerning the expression response of human T-cells to PMA and ionomicin treatment.
منابع مشابه
Small-Sample Analysis and Inference of Networked Dependency Structures from Complex Genomic Data
plications in Genetics and Molecular Biology 4: Article 32. Juliane Schäfer und Korbinian Strimmer. 2005. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754–764. Juliane Schäfer und Korbinian Strimmer. 2005. Learning large-scale graphical Gaussian models from genomic data. Summary The present work is concerned with modeling and inferring geneti...
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