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Gene Regulatory Network Inference: A Kernel-Based Learning Approach

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If you have a question about this talk, please contact Zoubin Ghahramani.

One of the central challenges of modern biology is to understand the dynamic architecture of gene regulatory networks. To address this problem, we introduce a framework for large-scale nonlinear system identification derived from kernel learning theory. The proposed inference technique is based on a nonparametric differential equation model of mRNA transcription and has been used to successfully reverse engineer a diverse class of synthetic gene regulatory networks. For synthetic networks, we derive estimates for both the time scale of mRNA degradation as well as the post-transcriptional time delay related to protein synthesis and activation.

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

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