  Transcription factors (TFs) play a vital role in the regulation of gene
expression thereby making them critical to many cellular processes. In this
study, we used graph machine learning methods to create a compendium of TF
cascades using data extracted from the STRING database. A TF cascade is a
sequence of TFs that regulate each other, forming a directed path in the TF
network. We constructed a knowledge graph of 81,488 unique TF cascades, with
the longest cascade consisting of 62 TFs. Our results highlight the complex and
intricate nature of TF interactions, where multiple TFs work together to
regulate gene expression. We also identified 10 TFs with the highest regulatory
influence based on centrality measurements, providing valuable information for
researchers interested in studying specific TFs. Furthermore, our pathway
enrichment analysis revealed significant enrichment of various pathways and
functional 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 targets
for therapeutic intervention in diseases associated with dysregulation of
transcription factors. We have released the dataset, knowledge graph, and
graphML methods for the TF cascades, and created a website to display the
results, which can be accessed by researchers interested in using this dataset.
Our study provides a valuable resource for understanding the complex network of
interactions between TFs and their regulatory roles in cellular processes.

Molecular Networks;Machine Learning