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Published February 1, 2022 | Version Version 0.1
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Supplementary material - A data science approach for the identification of molecular signatures of aggressive cancers.

  • 1. Medical University of Vienna
  • 2. Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil;
  • 3. Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, FIOCRUZ, Rio de Janeiro, Brazil;
  • 4. Laboratório Interdisciplinar de Pesquisas Médicas–Instituto Oswaldo Cruz, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil.

Description

Supplementary material - A data science approach for the identification of molecular signatures of aggressive cancers.

  • Supplementary Table 1: List of R dependency packages.
  • Supplementary Table 2:  Gene members of each pathway listed from the literature and extracted from KEGG, Biocarta, and Reactome databases. 
  • Supplementary Table 3: Gene members with their number of connections, frequency of overexpression, sample number, normalized counts, and normalized connections.
  • Supplementary Table 4: Genes with their normalized connections per type of cancer and aggressivity..
  • Supplementary Table 5: Mean, Std. deviation, and variance of normalized counts from target target genes between H and L classes.
  • Supplementary Table 6: Genes that are specific to each cancer classe.
  • Supplementary Table 7: Sub interactome of proteins corresponding to the genes of Tables 3 and 4 (except H2AFZ and TMEFF2 ) as well as Figure 5.
  • Supplementary Table 8: Kruskal-Wallis test by pathway.
  • Supplementary Table 9: Wilcoxon test of cancer types per pathway.
  • Supplementary Table 10: Correlations between entropies, PC1, and PC2. 
  • Supplementary Table 11: Percentage of contribution for up-regulated genes to the PCA’s first, second, and third components.
  • Supplementary Table 12: Gene importance as defined by RFC.

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