KELLER: estimating time-varying interactions between genes. MOTIVATION: Gene regulatory networks underlying temporal processes, such as the cell cycle or the life cycle of an organism, can exhibit significant topological changes to facilitate the underlying dynamic regulatory functions. Thus, it is essential to develop methods that capture the temporal evolution of the regulatory networks. These methods will be an enabling first step for studying the driving forces underlying the dynamic gene regulation circuitry and predicting the future network structures in response to internal and external stimuli. RESULTS: We introduce a kernel-reweighted logistic regression method (KELLER) for reverse engineering the dynamic interactions between genes based on their time series of expression values. We apply the proposed method to estimate the latent sequence of temporal rewiring networks of 588 genes involved in the developmental process during the life cycle of Drosophila melanogaster. Our results offer the first glimpse into the temporal evolution of gene networks in a living organism during its full developmental course. Our results also show that many genes exhibit distinctive functions at different stages along the developmental cycle. AVAILABILITY: Source codes and relevant data will be made available at http://www.sailing.cs.cmu.edu/keller.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Wang, Yuhao; Segarra, Santiago; Uhler, Caroline: High-dimensional joint estimation of multiple directed Gaussian graphical models (2020)
- Yang, Jilei; Peng, Jie: Estimating time-varying graphical models (2020)
- Hirayama, Jun-ichiro; Hyvärinen, Aapo; Ishii, Shin: Sparse and low-rank matrix regularization for learning time-varying Markov networks (2016)
- Song, Le; Kolar, Mladen; Xing, Eric P.: KELLER: estimating time-varying interactions between genes (2009) ioport