arXiv Machine Learning By Gonzalo A. Ruz

Continuous surrogates versus threshold Boolean networks for modeling Arabidopsis ISR gene regulation

Read the original on arXiv Machine Learning →

arXiv:2607. 23289v1 Announce Type: cross Abstract: Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability.

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arXiv Machine Learning
Sep 18

A Learning Algorithm for Threshold Boolean Networks with Prescribed Fixed Points

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.

By Gonzalo A. Ruz
arXiv AI
Aug 10

Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling

arXiv:2608. 06824v1 Announce Type: cross Abstract: A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and biological networks provide valuable priors on gene relationships.

By Quanquan Li, Yihe Chi, Liuyang Song, Hongbo Zhang, Jingyu Li, Xidong Xi, Conghua Wei, Yijie Sun, Yu Chen, Xin Liu, Qi Hu, Jing Ke, Guitao Cao