Jiawei Huang

deepManReg: a deep manifold-regularized learning model for improving phenotype prediction from multi-modal data

This paper introduces deepManReg, a deep learning model that improves phenotype prediction from multi-modal data by aligning features into a common latent space and using cross-modal manifolds for regularization.

Manifold learning analysis suggests novel strategies for aligning single-cell multi-modalities and revealing functional genomics for neuronal electrophysiology

The paper uses manifold learning to align single-cell transcriptomic and electrophysiological data, revealing gene expression-electrophysiology relationships and developmental trajectories in mouse neurons.

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

scGRNom predicts cell-type disease genes and regulatory networks from multi-omics data, revealing shared and disease-specific functions in schizophrenia and Alzheimer's Disease.