Samuel C. Mok

This paper introduces LRnetST, a statistical method for inferring ligand-receptor interaction networks from spatial transcriptomic data of ovarian tumors, revealing common and distinct interactions across samples.

This paper shows that molecular subtypes and gene signatures in ovarian cancer are influenced by stromal cell content, suggesting that bulk tissue analysis may be unreliable without accounting for cell admixture.

The paper develops PSIONIC, a machine learning method that integrates chromatin accessibility and RNA-seq data to infer transcriptional programs in gynecologic and basal breast cancers, revealing regulatory differences and survival associations.