Fei He

This study uses cross-frequency multilayer network analysis to show increased low-frequency coupling and decreased high-frequency coupling in Alzheimer's disease compared to healthy controls, improving classification performance.

The paper develops and compares four dynamical models of a pathogen-infected plant gene regulatory network to determine the best model for capturing its dynamics.

This study evaluates graph neural networks for Alzheimer's disease classification using EEG-based functional connectivity measures, finding that GNNs outperform baselines but no single FC measure consistently dominates.

This paper introduces a kernel-based nonlinear manifold learning method for EEG channel selection to distinguish Alzheimer's disease patients from healthy controls.

This paper reviews advances in nonlinear system identification for neural systems, emphasizing its importance for understanding neural functions and developing diagnostic tools.

GpABC is a Julia package that implements ABC methods with Gaussian process emulation for parameter inference and model selection in systems biology models.

This paper compares parametric and non-parametric methods for gene regulatory network inference using gradient matching and finds parametric methods often provide better results.