Laura Cantini

TL;DR: In this paper, the authors proposed a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes, and applied a consensus clustering algorithm based on state-of-the-art community detection methods.

TL;DR: An analytical pipeline, microRNA master regulator analysis (MMRA), developed to search for microRNAs potentially driving CRC subtypes is described, showing that, by combining statistical tests, target prediction and network analysis, MMRA effectively identifies micro RNAs functionally associated to cancer subtypes.

TL;DR: A review of a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples and discusses the emerging ICA applications to the integrative analysis of multi-level omics datasets.

TL;DR: An informative collection of gene signatures shown to be informative for cancer studies and reflecting mechanisms of cancer progression are selected and integrated into InfoSigMap, a new data visualization resource for the interpretation of the results of omics data analyses, which facilitates getting an insight into the mechanisms driving cancer.

TL;DR: In this paper, the authors performed a systematic evaluation of nine representative joint dimensionality reduction (jDR) methods using three complementary benchmarks and found that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts.