Juliet Luft

This paper uses machine learning to predict genomic alterations from H&E images of liver tumors, revealing genotype-phenotype links and highlighting genetic background limitations.

This study shows that genetic background and environmental factors determine the trajectory of cancer evolution in mice, with distinct mutational paths despite similar final tumor states.

The paper investigates how strand-asymmetric processes like replication and transcription influence DNA damage, repair, and mutagenesis, revealing mechanisms that shape cancer genome evolution.

The paper identifies genetic polymorphisms preferentially selected during cancer development by analyzing allele retention patterns in TCGA whole exome sequencing data.

The paper shows splicing enhances expression of AT-rich genes by increasing mRNA stability and cytoplasmic localization, while GC-rich genes are less affected by splicing.