Weighted-Lasso for Structured Network Inference from Time Course Data

We present a weighted-Lasso method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own a prior internal structure of connectivity which drives the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks, first yeast cell cycle regulation network by analyzing Spellman et al’s dataset and second E. coli S.O.S. DNA repair network by analysing U. Alon’s lab data.

💡 Research Summary

The paper introduces a weighted‑Lasso framework for inferring the parameters of a first‑order vector autoregressive (VAR(1)) model that describes gene‑expression time‑course data generated by directed gene‑to‑gene regulatory networks. Traditional Lasso applies a uniform L1 penalty to all regression coefficients, which ignores the fact that real biological networks often exhibit non‑uniform connectivity patterns such as modules, pathways, or hub‑centric structures. To incorporate this prior structural information, the authors propose assigning a weight (w_{ij}) to each potential edge ((i\rightarrow j)) and solving

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