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Orthologous networks in biological systems

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How an organism develops and responds to environmental stress is influenced by its gene regulatory network (GRN). Here I outline nonparametric Bayesian approaches to network inference based upon Gaussian processes, that allows networks to be inferred from multiple time series transcriptomic datasets. These approaches have proven to be highly successful at identifying important regulators in a variety of processes in Arabidopsis thaliana. Furthermore, these methods address another key challenge in network inference: how to infer networks from multiple datasets when the underlying networks are expected to be similar, but non-identical. This may be the case when networks are rewired in different treatments (treatment specific networks), where network differences arise due to genetic differences (individual or patient specific networks) and (iii) where data is collected in different but related species.

This talk is part of the Machine Learning @ CUED series.

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