ArticleInterface focus2025
Identification of models describing gene expression data leveraging machine learning methods.
Article in Interface focus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Mechanistic ordinary differential equation models of gene regulatory networks are a valuable tool for understanding biological processes that occur inside a cell, and they allow for the formulation of novel hypotheses on the mechanisms underlying these processes. Although data-driven methods for inferring these mechanistic models are becoming more prevalent, it is often unclear how recent advances in machine learning can be used effectively without jeopardi zing the interpretability of the resulting models. In this work, we present a framework to leverage neural networks for the identification of data-driven models for time-dependent intracellular processes, such as cell differentiation. In particular, we use a graph autoencoder model to suggest novel connections in a gene regulatory network. We show how the improvement of the graph suggested using this neural network leads to the generation of hypotheses on the dynamics of the resulting identified dynamical system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.