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Gene Regulatory Network Inference: A Kernel-Based Learning Approach

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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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