ArticleThe Canadian journal of statistics = Revue canadienne de statistique2022
A nonlinear sparse neural ordinary differential equation model for multiple functional processes.
Article in The Canadian journal of statistics = Revue canadienne de statistique, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.Bioinformatics (Oxford, England) · 2025Article
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Abstract
In this article, we propose a new sparse neural ordinary differential equation (ODE) model to characterize flexible relations among multiple functional processes. We characterize the latent states of the functions via a set of ordinary differential equations. We then model the dynamic changes of the latent states using a deep neural network (DNN) with a specially designed architecture and a sparsity-inducing regularization. The new model is able to capture both nonlinear and sparse dependent relations among multivariate functions. We develop an efficient optimization algorithm to estimate the unknown weights for the DNN under the sparsity constraint. We establish both the algorithmic convergence and selection consistency, which constitute the theoretical guarantees of the proposed method. We illustrate the efficacy of the method through simulations and a gene regulatory network example.
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