Guangyou Duan

This study uses machine learning to distinguish rare disease cases from controls using observed and excluded phenotypes, finding that Random Forest performs best and both specificity and occurrence count are important for feature selection.

The paper identifies coordinate up-regulation of TK1 and TS in cycling cells and shows it is essential for dTTP supply, supporting dual-targeted cancer therapy.

This study discovers a Notch/snRNA negative feedback regulation, linking cell signaling to RNA splicing and offering therapeutic implications for Alzheimer's disease.

The paper proposes a two-route model for SARS-CoV-2 RTC function in jumping transcription, identifies a motif for METTL3 recognition, and reports TRS hairpins for NSP15 cleavage and RNA methylation.

The paper identifies recombination regions and mutation sites in SARS-CoV-2, SARS-CoV, and SARS-like CoVs, linking specific genetic features to pandemic emergence.

The paper proposes a negative feedback model explaining SARS-CoV-2 replication and transcription regulation through nsp15 cleavage of transcription regulatory sequences.