Haohan Wang

Joint modeling of multi-timepoint spatial observations for time-resolved spatial-unit-specific gene regulatory network inference

This paper introduces SpaTemGRN, a method for inferring time-resolved spatial-unit-specific gene regulatory networks from spatiotemporal transcriptomics data, showing improved performance over existing methods.

Spatially varying cell-specific gene regulation network inference

SVGRN infers cell-specific gene regulatory networks from spatial transcriptomics data, showing improved performance in noisy and complex datasets.

Heteroscedastic Personalized Regression Unveils Genetic Basis of Alzheimer's Disease Stratified by Cognitive Level

This paper introduces Het-PR, a method to identify personalized genetic factors in Alzheimer's disease by capturing inter-sample heterogeneity, and finds different SNPs associated with cognitive levels.

Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets

This paper introduces a method to jointly analyze two independent GWAS datasets with different phenotypes, addressing confounding factors and showing its utility in studying Alzheimer's disease and substance use disorder.

Supervised Adversarial Alignment of scRNA-seq Data

This paper develops a domain adversarial neural network to reduce scRNA-seq data dimensionality while minimizing batch effects, improving cell type separation across datasets.

Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

This paper introduces a system using deep reinforcement learning to automatically curate a genetic association database by simulating human research behaviors.

Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications

This paper introduces a method to remove confounding factors in deep neural networks to improve prediction accuracy in healthcare applications.

Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies

This paper develops a truncated-rank sparse linear mixed model for variable selection in heterogeneous datasets, demonstrating its effectiveness on genomic data from plants, mice, and humans.

Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model

This paper introduces a Squared-LMM model to correct confounding factors in genomic association mapping, showing improved accuracy in predicting trait-locus associations using synthetic and real data.

Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes

The paper evaluates regularized linear mixed effect models for correcting multiple confounders in biological data, finding that sequence-based correction performs best when confounders contribute equally.