yunchuankong/gedfn

GEDFN: Graph-Embedded Deep Feedforward Network

GEDFN

GEDFN: graph-embedded deep feedforward networks - Tensorflow implementation

The method is introduced in https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty429/5021680?redirectedFrom=fulltext.

Prerequisites

The following packages are required for executing the main code file:

• NumPy http://www.numpy.org/
• Scikit-learn http://scikit-learn.org/stable/install.html
• Tensorflow https://www.tensorflow.org/install/

Usage

Data formats

• data matrix (example_expression.csv): a csv file with n rows and p+1 columns. n is the number of samples and p is the number of features (continuous variables, such as gene expression values). The additional column at last is the 0/1 binary outcome variable vector. n=100 and p=500 for the example dataset.
• feature graph (example_adjacency.txt): a txt file with p rows and p colunms, which is the corresponding adjacency matrix of the feature graph.

NOTE: no headers are allowed in both files.

Run GEDFN

In the terminal, change the directory to the folder under which main.py is located, then type the command

 python main.py "example_expression.csv" "example_adjacency.txt" "var_impo.csv"

where var_impo.csv is the output file for variable importance and will be created by the program automatically. The program will run while printing logs

Epoch: 1 cost = 0.619800305 Training accuracy: 0.5  Training auc: 0.658
Epoch: 2 cost = 0.620009381 Training accuracy: 0.5  Training auc: 0.728
Epoch: 3 cost = 0.610391283 Training accuracy: 0.5  Training auc: 0.782
......
Epoch: 71 cost = 0.142398462 Training accuracy: 0.988  Training auc: 0.999
Epoch: 72 cost = 0.126102197 Training accuracy: 0.988  Training auc: 0.999
Epoch: 73 cost = 0.116139328 Training accuracy: 0.988  Training auc: 1.0
Epoch: 74 cost = 0.121380727 Training accuracy: 0.988  Training auc: 1.0
Epoch: 75 cost = 0.127119239 Training accuracy: 1.0  Training auc: 1.0
Epoch: 76 cost = 0.097086006 Training accuracy: 1.0  Training auc: 1.0
Early stopping.
*****===== Testing accuracy:  0.85  Testing auc:  0.94 =====*****

and the var_impo.csv file is seen in this repo.

Hyperparameters and training options

Seen in the main.py.

gedfn's Issues

Where did the input data set come from?

What kind of disease gene expression data is the input data?