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A systems biology approach to identify perturbed processes driving cancer

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A major challenge in cancer biology is the identification of the genetic alterations promoting cancer. Current methods tackle this complex task by detecting signs of positive selection and searching for “mountains” and “hills” in the landscape of cancer alterations. However, in cancers where the mountains and hills are very rare and the mutational landscape is highly variable but mostly flat, these approaches are clearly insufficient. A novel, patient-specific approach, named sysSVM, towards the comprehensive characterisation of genes contributing to cancer have been developed. sysSVM applies machine learning and systems biology to identify new cancer genes based on how similar they are to known cancer genes rather than how frequently their alterations occur across samples. This allows patient-specific predictions. In her seminar, Professor Ciccarelli will describe the features of sysSVM and its application to samples from the OCCAMS Consortium to identify the perturbed processes that drive oesophageal cancer.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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