Siyuan Chen

An AI Agent for Fully Automated Multi-omic Analyses

AutoBA is an AI agent that automates multi-omic analyses using large language models, offering adaptability and security across various sequencing and omics data types.

PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients' privacy in omic data

PPML-Omics is a privacy-preserving federated learning method that balances privacy protection and model utility across three omic data tasks.

Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

CLEAR is a self-supervised contrastive learning framework for scRNA-seq that improves clustering, batch correction, and pseudo-time inference by reducing technical noise and heterogeneity.

Deep learning identifies and quantifies recombination hotspot determinants

This paper introduces RHSNet, a deep learning method that identifies and quantifies recombination hotspot determinants across species, sexes, and populations.

HiC-LDNet: A general and robust deep learning framework for accurate chromatin loop detection in genome-wide contact maps

HiC-LDNet is a deep learning framework for chromatin loop detection in Hi-C data, achieving high accuracy in both bulk and sparse single-cell Hi-C maps.