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Papers: 10.1186/s12920-019-0628-y

https://doi.org/10.1186/s12920-019-0628-y

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

Cited by: 14
Author(s): Léon-Charles Tranchevent, Francisco Azuaje, Jagath C. Rajapakse
Published: over 5 years ago

Software Mentions 10

cran: igraph
Network Analysis and Visualization
Papers that mentioned: 1,555
Very Likely Science (100)
cran: randomForest
Breiman and Cutler's Random Forests for Classification and Regression
Papers that mentioned: 1,447
Very Likely Science (100)
cran: SNFtool
Similarity Network Fusion
Papers that mentioned: 9
Very Likely Science (90)
cran: WGCNA
Weighted Correlation Network Analysis
Papers that mentioned: 3,479
Very Likely Science (85)
pypi: graph-tool
An efficient python module for manipulation and statistical analysis of graphs.
Papers that mentioned: 5
Very Likely Science (90)
pypi: igraph
High performance graph data structures and algorithms
Papers that mentioned: 1,555
Very Likely Science (100)
pypi: networkx
Python package for creating and manipulating graphs and networks
Papers that mentioned: 110
Very Likely Science (100)
pypi: numpy
Fundamental package for array computing in Python
Papers that mentioned: 832
Very Likely Science (100)
pypi: pandas
Powerful data structures for data analysis, time series, and statistics
Papers that mentioned: 640
Very Likely Science (100)
pypi: scipy
Fundamental algorithms for scientific computing in Python
Papers that mentioned: 1,071
Very Likely Science (100)