Yen‐Yi Lin

This study uses longitudinal scRNA-seq to track neuroendocrine transdifferentiation in a prostate cancer model, identifying transcriptional changes and key regulators during treatment-induced NEPC progression.

This study uses longitudinal single-cell RNA sequencing to identify RUNX1T1 as a key regulator in the transition and maintenance of treatment-induced neuroendocrine prostate cancer.

This study identifies rare germline variants associated with metastatic prostate cancer using an extreme phenotype cohort and validates their functional impact.

scMUSCL improves scRNA-seq clustering by transferring knowledge from multiple annotated datasets without requiring batch correction or prior cluster number knowledge.

This study identifies lineage-specific transcription factor profiles in prostate cancer progression, revealing dynamic shifts during NE transdifferentiation and proposing a three-phase model.

This paper identifies m-APTA, an MTA analogue that reduces NBA toxicity without hypothermia, improving safety for MTAP-deficient cancer treatment.

ScRAT predicts clinical phenotypes from scRNA-seq data using attention-based neural networks, outperforming existing methods with limited samples and identifying critical cell types.

MechRNA predicts lncRNA mechanisms by integrating RNA-RNA and RNA-protein interaction predictions to identify potential targets and mechanisms for cancer-related lncRNAs.