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Transcription factors (TFs) play a vital role in the regulation of geneexpression thereby making them critical to many cellular processes. In thisstudy, we used graph machine learning methods to create a compendium of TFcascades using data extracted from the STRING database. A TF cascade is asequence of TFs that regulate each other, forming a directed path in the TFnetwork. We constructed a knowledge graph of 81,488 unique TF cascades, withthe longest cascade consisting of 62 TFs. Our results highlight the complex andintricate nature of TF interactions, where multiple TFs work together toregulate gene expression. We also identified 10 TFs with the highest regulatoryinfluence based on centrality measurements, providing valuable information forresearchers interested in studying specific TFs. Furthermore, our pathwayenrichment analysis revealed significant enrichment of various pathways andfunctional categories, including those involved in cancer and other diseases,as well as those involved in development, differentiation, and cell signaling.The enriched pathways identified in this study may have potential as targetsfor therapeutic intervention in diseases associated with dysregulation oftranscription factors. We have released the dataset, knowledge graph, andgraphML methods for the TF cascades, and created a website to display theresults, which can be accessed by researchers interested in using this dataset.Our study provides a valuable resource for understanding the complex network ofinteractions between TFs and their regulatory roles in cellular processes.

Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning