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<p>Gene regulatory network (GRN) reconstruction is a fundamental challenge in computational biology, and is crucial for understanding gene interactions. In this study, we aim to incorporate Gene Ontology (GO) similarities into the construction of GRNs. Our key assumption is that genes with higher similarity in Molecular Function, Biological Process, or Cellular Component categories are more likely to be functionally related and, therefore, more likely to be connected in the network. We introduce <Emphasis FontCategory="NonProportional">SimMapNet</Emphasis>, a Bayesian framework that estimates the precision matrix, which serves as the adjacency matrix in a Gaussian Graphical Model for undirected GRN inference. <Emphasis FontCategory="NonProportional">SimMapNet</Emphasis> enhances network inference by integrating GO similarities, which inform the hyperparameters of the prior distribution through a kernel function, incorporating biological prior knowledge in a principled manner. We evaluate <Emphasis FontCategory="NonProportional">SimMapNet</Emphasis> on three datasets: two datasets from the SOS DNA-repair response pathway in <i>Escherichia coli</i> and one dataset from <i>Drosophila melanogaster</i>. The results demonstrate the algorithm’s superior performance compared to state-of-the-art methods such as GLASSO, GENIE3, and KBOOST in terms of F1-score. <Emphasis FontCategory="NonProportional">SimMapNet</Emphasis> has low time complexity, making it suitable for constructing large networks. Our simulation results confirm that <Emphasis FontCategory="NonProportional">SimMapNet</Emphasis> is particularly well-suited for scenarios with limited sample sizes, where traditional methods often struggle.</p>

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SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint

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  • 发表日期：

    2026年6月26日

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  • Maryam Shahdoust,
  • Rosa Aghdam,
  • Mehdi Sadeghi

摘要

Gene regulatory network (GRN) reconstruction is a fundamental challenge in computational biology, and is crucial for understanding gene interactions. In this study, we aim to incorporate Gene Ontology (GO) similarities into the construction of GRNs. Our key assumption is that genes with higher similarity in Molecular Function, Biological Process, or Cellular Component categories are more likely to be functionally related and, therefore, more likely to be connected in the network. We introduce SimMapNet, a Bayesian framework that estimates the precision matrix, which serves as the adjacency matrix in a Gaussian Graphical Model for undirected GRN inference. SimMapNet enhances network inference by integrating GO similarities, which inform the hyperparameters of the prior distribution through a kernel function, incorporating biological prior knowledge in a principled manner. We evaluate SimMapNet on three datasets: two datasets from the SOS DNA-repair response pathway in Escherichia coli and one dataset from Drosophila melanogaster. The results demonstrate the algorithm’s superior performance compared to state-of-the-art methods such as GLASSO, GENIE3, and KBOOST in terms of F1-score. SimMapNet has low time complexity, making it suitable for constructing large networks. Our simulation results confirm that SimMapNet is particularly well-suited for scenarios with limited sample sizes, where traditional methods often struggle.