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Table 3 Best performing DNN architectures.

From: A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

ConfigurationArchitectureBalanced accuracy
Clinical outcome = ‘Death from disease
Fischer-M, centralities[8,8,8,2]87.3%
Fischer-M, modularities[8,4]83.9%
Fischer-M, both[8,8,8]86.8%
Fischer-R, centralities[8,8,8,4]85.8%
Fischer-R, modularities[8,8,8,2]82.1%
Fischer-R, both[2,2,2,2]85.2%
Fischera, centralities[8,2,2]86.1%
Fischera, modularities[8,2,2]84.7%
Fischera, both[8,8,4]84.7%
Clinical outcome = ‘Disease progression
Fischer-M, centralities[8,8,8,2]84.3%
Fischer-M, modularities[8,8,2]82.3%
Fischer-M, both[4,4,2]83.7%
Fischer-R, centralities[8,8,4]83.7%
Fischer-R, modularities[8,2,2]79.1%
Fischer-R, both[8,8,8,8]77.9%
Fischera, centralities[4,2,2,2]84.7%
Fischera, modularities[8,8]79.6%
Fischera, both[4,2]81.5%
  1. One row corresponds to the best model for a given clinical outcome and configuration (from Table 2). The best performance (i.e., balanced accuracy) is displayed in bold for each clinical outcome
  2. aCombined dataset in which the topological features of both ‘Fischer-M’ and ‘Fischer-R’ are concatenated