Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach

ANNALS OF APPLIED STATISTICS, pp. 1311-1333, 2016.

Cited by: 6|Views0

Abstract:

Biological structure and function depend on complex regulatory interactions between many genes. A wealth of gene expression data is available from high-throughput genome-wide measurement technologies, but effective gene regulatory network inference methods are still needed. Model-based methods founded on quantitative descriptions of gene ...More

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