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arXiv Machine Learning By Gonzalo A. Ruz

A Learning Algorithm for Threshold Boolean Networks with Prescribed Fixed Points

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The paper introduces a learning algorithm for threshold Boolean networks (TBNs) that can infer networks with a specified set of fixed points. The method uses a custom differentiable loss function to enforce fixed point preservation, penalize spurious attractors, encourage binary outputs, and promote sparsity via L1 regularization. When applied to the FOS-GRN model of Arabidopsis thaliana, the algorithm perfectly reconstructed all 10 desired fixed points in 5 of 30 runs and on average recovered 8.53 ± 0.90 correct fixed points without any spurious attractors, outperforming standard approaches such as the Perceptron and Logistic Regression.

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