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The authors would like to thank Professor Ellen Zwarthoff for sharing details on the mutation data by Kompier et al., and for giving us useful comments.
We analyzed mutation data by applying MutSigCV v.1.4.
Somatic mutation data by whole exome sequencing were available on 463 cases.
On a related note, Supplementary Figure S7 shows enrichment of the networks inferred from somatic mutation data by either multinomial FuseNet or Mult-GM.
Parameter settings for each method are given in Supplementary Appendix C. Also, because SciClone runtimes were extremely long, we down sampled the mutation data by a factor of 20.
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Pathway analysis typically correlates a given set of molecular changes (e.g., differential expression, mutation, and copy number variation data) by projecting them onto well-characterized biological pathways.
All these developments call for an accelerated and versatile approval system to allow clinical applications to follow the rapid accumulation of mutation data provided by new generation sequencing.
For the collectively inferred network, we consider both gene expression profiles and somatic mutation data provided by the ICGC assuming the Poisson model for the RNA-seq data and the multinomial model for the mutation data.
The RNA interference-based functional genomic screening approaches can experimentally test the phenotypic effect of silencing large numbers of genes individually and may support the interpretation of mutation data sets by identifying genes that influence cellular fitness or drug sensitivity.
With an increasing amount of both protein structural data in the PDB database and somatic mutation data generated by next-generation sequencing (NGS) experiments, the integration of protein structural information and large-scale somatic mutations offers an alternative, promising approach to uncovering functionally important somatic mutations in cancer.
We work with human cancer mutation data as given by G reenman et al. (2007).
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