Publications

Scientists proposed a framework to investigate the genomic alterations in neuroblastoma subtypes and identified TEAD4, a transcription factor, as a novel target for therapy.

Researchers at the UCSD CTD² Center created a parsimonious composite network (PCNet), which has high efficiency and performance over any single network.

Broad Institute CTD² scientists developed a bioinformatic approach, RWEN, that predicts the responses of human cancer cell lines to a panel of compounds using the gene-expression profiles.

CTD² researchers at UCSF-1 present a quantitative map linking the influence of chemotherapeutic agents to tumor genetics. This chemical-genetic interaction map can aid in identifying new factors that dictate responses to chemotherapy and prioritize drug combinations.

Scientists developed metaVIPER to assess protein activity in orphan tissues and single cells by integrative analysis of multiple, non-tissue-matched regulatory models. This approach could help to identify critical dependencies within molecularly heterogeneous sub-populations of cancer tissues.

Researchers developed MethylMix 2.0, an algorithm implemented in R that facilitates automated downloading and preprocessing of DNA methylation and gene expression datasets from pan-cancer studies. The tool can be used to identify disease specific hyper and hypomethylated genes and cancer subtyping.

Researchers at Johns Hopkins School of Medicine CTD² Center use multidisciplinary studies to define a set of mechanical properties and cell behaviors to study how cells collaborate to elongate epithelial tubes. These studies will enable the understanding of cell migration through tissues.