RESEARCH SUMMARY: Understanding the mechanisms which drive coordinate gene activation will provide critical biological insights into development and disease. This supplement aims to enable AI/ML methods to integrate diverse datasets to help achieve that goal. Specifically, we plan to computationally model the spatiotemporal regulation of genes in Drosophila by combining ChIP-Seq, Hi-C/ Micro-C, and mRNA-Seq datasets. Moreover, we will obtaining and model single-cell measurements (scRNA-Seq and scATAC-Seq) across early embryonic developmental stages when domains of coordinate gene activation are established. Therefore, we propose three tasks to produce AI/ML-ready datasets for the community. (1) Development of robust preprocessing pipelines for easy access and input to graph-based neural networks for integrating datasets. (2) Application of sub-graph selection methods to extract informative interactions from large and noisy graphs generated from chromatin capture technologies. (3) Integrating single-cell measurements and providing these combinations for data modeling and analysis purposes. The proposed methods will assist the goals of accurately capturing the underlying biological information when using various machine learning approaches. Furthermore, we will provide processed datasets and scripts to the community to enable methodological innovations. Dr. Larschan will collaborate with machine learning expert Dr. Singh and we have collaborated on several projects based on the proposed topic with joint students and publications. This supplement will be essential to allowing us to continue collaborating and generating new methods and data sets which will provide key insight into coordinate gene activation.