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Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.

Daniele RamazzottiAlex GraudenziLuca De SanoMarco AntoniottiGiulio Caravagna
Published in: BMC bioinformatics (2019)
We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • electronic health record
  • big data
  • squamous cell carcinoma
  • genome wide
  • machine learning
  • gene expression
  • mass spectrometry
  • artificial intelligence