Haoyu Chao

Cross-Species Prediction of Histone Modifications in Plants via Deep Learning

This study uses deep learning to predict histone modifications across plant species, showing improved cross-species performance with family-level models.

CellReasoner: A reasoning-enhanced large language model for cell type annotation

CellReasoner is a lightweight LLM for cell type annotation that uses reasoning to map gene expression to labels with zero- and few-shot generalization.

HPInet: Interpretable prediction of Host-Pathogen protein-protein Interactions using a transformer-based neural network

HPInet predicts host-pathogen protein-protein interactions using a transformer-based model, achieving high accuracy and identifying key residues from sequence data.

Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language Features

This paper introduces T6CNN, a model that improves T6SE prediction by integrating signal sequences, evolutionary data, and pre-trained features, achieving higher accuracy, sensitivity, and specificity than existing tools.

SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research

SoyOD integrates soybean multi-omics data and provides tools for gene mining and analysis, with a focus on agronomic traits like plant height.

iSeq: An integrated tool to fetch public sequencing data

iSeq is a tool for retrieving public sequencing data from multiple databases, supporting various accession formats and enabling parallel downloads and metadata retrieval.

Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition

This study systematically analyzes single-cell data to show how transposable elements dynamically regulate the endothelial-to-hematopoietic transition through epigenetic changes and inflammatory signaling.

T4SEpp: a pipeline integrated with protein language models effectively predicting bacterial type IV secreted effectors

T4SEpp predicts bacterial type IV secreted effectors using protein language models and integrates multiple modules, achieving high sensitivity and specificity.