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NetNorM: capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

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Academic year: 2021

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Figure 1 – Overview of NetNorM. (a) Using a gene network as background knowledge (lower left), NetNorM normalises each mutation profile in a collection of somatic mutation profiles (upper left) into a new, binary representation (right) which encodes additi
Figure 2 – Comparison of the survival predictive power of the raw mutation data, NSQN and NetNorM (with Pathway Commons as gene network) for 8 cancer types
Figure 4 – Genes frequently selected in the survival prediction model for LUAD (left) and SKCM (right) learned using the NetNorM representation of mutations with Pathway Commons as gene network
Figure 5 – Analysis of predictive genes. (a) Comparison of survival prediction performances according to patients’ mutation rate for LUAD
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