Chao Zhang

Scaling genome annotation across the eukaryotic tree of life with OrionGeno

OrionGeno annotates over 5,300 eukaryotic genomes using a phylogeny-aware deep learning model, outperforming existing methods in gene and protein structure prediction.

DomDiff: protein family and domain annotation via diffusion model and ESM2 embedding

DomDiff improves domain boundary identification and classification using a diffusion model with ESM2 embeddings, outperforming existing methods and excelling in rare families.

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Stereopy provides a framework for analyzing multi-sample spatial transcriptomics data to model comparative and spatiotemporal cellular heterogeneity through three key algorithms.

BatchEval Pipeline: Batch Effect Evaluation Workflow for Multiple Datasets Joint Analysis

BatchEval Pipeline evaluates batch effects in multi-dataset transcriptomics analysis and provides a report to guide batch correction.

spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

spatiAlign integrates spatially resolved transcriptomics datasets using contrastive learning, improving batch effect removal and enabling downstream analysis across multiple tissue sections.

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Spatial-ID improves cell type annotation in spatial transcriptomics by combining transfer learning and spatial embedding, outperforming existing methods on public datasets.

Generative pretraining from large-scale transcriptomes: Implications for single-cell deciphering and clinical translation

The paper introduces tGPT, a generative model for integrating large-scale single-cell transcriptomes, showing its effectiveness in clustering, trajectory inference, and linking gene patterns to clinical outcomes.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings

The paper introduces iSEEEK, a method for integrating large-scale single-cell transcriptomes using gene rankings, showing good clustering and transferability across datasets.