Descripotion:

This repository contains the data and MATLAB code for the "Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using unsupervised machine Learning method":

Data sets:

File "Candidate_Genes.xlsx" contains 1,300 Genes as a candidate set of COVID-19 related genes.

File "Signaling_Pathways.xlsx" contains KEGG signaling pathways with more than 8 genes related to COVID-19.

File "Disease_Pathways.xlsx" contains KEGG disease pathways with more than 8 genes related to COVID-19.

To calculate 6 informative features for pathways from biological network related to COVID-19 pathways, run an algorithm "Topological_feature.m".

To run the algorithm follow the "Read_me.txt" steps.

See the output at "Output.txt" file.

To select the top significant pathways related to COVID-19, use the Laplacian Score values with respect to "Laplacian_Score_Feature_Selection.m" algorithm (see "Read_me.txt" file).

Link of paper:

https://www.sciencedirect.com/science/article/pii/S1568494622005968

Contact:

Please do not hesitate to contact me if you have any question.

Email: [email protected]

Please cite us if you find this study helpful.