MOESM2 of Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks

Additional file 2: Table S1. 5-fold valid test accuracy about various networks

MOESM1 of Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks

Additional file 1: Figure S1. Experiments Networks (A) Final Network consists of Embedding Layer, LSTM Layer, 4 Fully Connected Layers. (B) Dropout0.3 Network adds Dropout Layer(0.3) between FC Layer and LSTM Layer on origin network. (C) Dropout0.5 Network adds Dropout Layer(0.5) between FC Layer and LSTM Layer on origin network. (D) Dropout0.7 Network adds Dropout Layer(0.7) between FC Layer and LSTM Layer on origin network. (E) Bignode network has randomly increased nodes in some Layers on the origin network

Additional file 2: of Systematic identification of an integrative network module during senescence from time-series gene expression

List of the identified common network information (XLSX 30 kb

Additional file 1: of Systematic identification of an integrative network module during senescence from time-series gene expression

The experimental results with MSC senescence dataset (DOC 708 kb

Additional file 1: of Metabolomics approach to serum biomarker for laxative effects of red Liriope platyphylla in loperamide-induced constipation of SD rats

Table S1. Concentration of 33 metabolites in serum (DOCX 19 kb