Ulisse: an R package to go beyond the boundaries of knowledge of molecular pathways. Alice Chiodi,Valentina Nale,Ettore Mosca Institute of Biomedical Technologies, National Research Council, via F.lli Cervi 93, Segrate, Milan, Italy Abstract Introduction. “Omics” assays typically yield relatively long gene lists whose interpretation is a major challenge for many researchers. Pathway analysis is a fundamental tool for explaining such lists. It provides mechanistic insights, translates gene-level findings into functional “blocks” that are easier to interpret, and helps reducing the biological heterogeneity at gene-level to common underlying mechanisms.
Short talk: RadioGx: a Package for Integrative Analysis of Cellular Features and Radiosensitivity in Cancer
RadioGx: a Package for Integrative Analysis of Cellular Features and Radiosensitivity in Cancer Ian Smith,Petr Smirnov,Benjamin Haibe-Kains University Health Network, University of Toronto Abstract While radiation therapy is an integral method for cancer treatment, clinical choices are not currently informed by the genetic and molecular profile of a patient’s tumour. Though it has been shown that genetic features implicate variability, the exact relationship between these features and radiosensitivity is poorly understood.
Short talk: PDATK: an R package for molecular classification and survival prediction in pancreatic ductal adenocarcinoma
PDATK: an R package for molecular classification and survival prediction in pancreatic ductal adenocarcinoma Christopher Bernard Eeles,Heewon Seo,Anthony Mammoliti,Benjamin Haibe-Kains Princess Margaret Cancer Research Center Abstract Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond.