Wenpin Hou

MUSTARD reduces dimensionality of multi-sample scRNA-seq data using trajectory guidance, revealing sample-level heterogeneity and associated gene markers.

GPTAnno provides a hierarchical cell type annotation method using GPT models and ontology trees, improving accuracy across species and tissues by integrating clustering with ontology constraints.

This paper benchmarks large language models for genomic knowledge using GeneTuring, finding that a custom GPT-4o setup with NCBI APIs performs best.

This study evaluates large multimodal models for one-shot learning and interpretability in biomedical image classification, showing their superiority over traditional methods.

The paper investigates DNA methylation changes during pancreatic acinar-ductal metaplasia (ADM) and their persistence without Kras mutation, linking epigenetic memory to signaling pathway alterations and cancer cell plasticity.

MethylProphet predicts whole-genome DNA methylation without methylation input using a gene-guided Transformer model trained on large-scale bisulfite sequencing data.

EpiFoundation is a foundation model for scATAC-seq that learns cell representations through peak-to-gene alignment, achieving state-of-the-art performance in downstream tasks.

This study evaluates GPT-4 for cell type annotation in single-cell RNA-seq, showing it can accurately annotate cell types using marker gene data and reducing manual effort.

Lamian is a statistical framework for analyzing pseudotime differences across multiple scRNA-seq samples, improving generalizability by accounting for sample variability.

GeneSegNet improves cell segmentation by integrating gene expression and imaging data, outperforming methods that neglect or underuse these inputs.

Paella decomposes spatial heterogeneity in cell trajectories from spatial transcriptomics data, identifying spatially varying genes linked to tumor progression.

Palo optimizes color palettes for single-cell and spatial genomic data to improve visualization by assigning distinct colors to spatially neighboring clusters.

This study evaluates 18 scRNA-seq imputation methods using cell line and tissue data, finding significant variability in performance and recommending caution in downstream analyses.

SCATE improves the accuracy of estimating cis-regulatory element activities in single-cell ATAC-seq data by integrating co-activated elements, similar cells, and regulome data.

The paper proves that controlling Boolean networks to attractors requires O(log2 N + log2 M) driver nodes, improving upon previous O(N) estimates.