Xabier Martinez‐de‐Morentin

scAEGAN unifies single-cell genomics data by adversarial learning of latent space correspondences, outperforming existing methods in integration and prediction tasks.

This paper introduces LIBRA, a neural network framework for integrating and predicting across paired single-cell multi-omics data, showing improved clustering resolution and predictive power.

scMomentum infers cell-type-specific regulatory networks and energy landscapes from single-cell transcriptomic data without needing temporal or perturbation experiments.