WMDS.net is an algorithm based on network control theory for identifying cancer driver genes. Compared with other methods and traditional differential gene statistical tests, WMDS.net offers higher accuracy, thereby reducing false positives (https://github.com/chaofen123/WMDS.net, https://doi.org/10.1093/bioinformatics/btad071). WMDS.netL is an improved and optimized version of WMDS.net, focusing specifically on the identification of cancer-driving lncRNAs during tumorigenesis and progression.
This repository includes the deployment code for WMDS.netL and related integration analysis codes. For the data used, if the file size meets GitHub's upload restrictions, it will also be included here (for files exceeding the size limit, acquisition methods will be provided). You can reproduce the results presented in our paper (to be published) using these codes. All codes are organized according to the sequence of figures in the paper, with brief comments at the beginning of each code file explaining its purpose and the final output.
Our codes are primarily constructed using MATLAB, R, and Python. The deployment of the WMDS.netL algorithm is based on MATLAB, while the integration analysis is completed using R and Python. Additionally, the repository contains a packaged workflow for an tissue distribution specificity algorithm based on Shannon entropy and a genomic conservation analysis algorithm based on sliding windows, both of which can be conveniently utilized.
We welcome anyone to use WMDS.netL for academic exploration in cancer biology, please cite our latest publication (to be published).
If you have any questions or would like to discuss ideas, feel free to contact us at: [email protected].
We are also very open to potential collaborations.