Yikang Li

This paper introduces BrainVAE, a transformer-based variational autoencoder, to classify preclinical Alzheimer's disease using white matter BOLD signals and finds it outperforms other models when combining white and gray matter data.

This paper benchmarks clustering, alignment, and integration methods for spatial transcriptomics using real and simulated datasets, providing recommendations and code for reproducibility.

MaskGraphene integrates multi-slice, multi-condition spatial transcriptomics data using a graph neural network to generate interpretable joint embeddings with preserved geometric information, improving downstream analyses.