The package is designed for interpreting high-throughput experimental data by leveraging gene interactions. Typical inputs of this program are two lists of genes, one contains a target list from an experiment and the other contains all the genes in the experimental array. The output of the program is list of pathways and their respective p-values. In the current version, the pathways are retrieved from KEGG. If you wish to use this package in your publication or for your project please cite the following publications: 1- P. Naderi Yeganeh and M. T. Mostafavi, "Causal Disturbance Analysis: A Novel Graph Centrality Based Method for Pathway Enrichment Analysis," in IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi: 10.1109/TCBB.2019.2907246 2- Naderi Yeganeh, Pourya, and M. Taghi Mostafavi. "Use of Structural Properties of Underlying Graphs in Pathway Enrichment Analysis of Genomic Data." In Proceedings of the 8th ACM International Conference on Bioinformatics,Computational Biology, and Health Informatics, pp. 279-284. ACM, 2017.
|Author||Pourya Naderi Yeganeh|
|Maintainer||Pourya Naderi Yeganeh <[email protected]>|
|Package repository||View on GitHub|
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