Summary/Abstract The scarcity of comprehensive pharmacogenomics resources poses a significant obstacle to the development of new therapies for pediatric cancers. Our parent R00 project seeks to overcome this challenge by creating and validating a novel deep learning model for predicting drug sensitivity of pediatric tumors using integrative multi- omics profiles. However, the utilization of cutting-edge deep learning models to analyze multi-omics is often challenging since the data are high-dimensional and unstructured. Under the parent project, we have evaluated several embedding methods to transform multi-omics data into a structured format that enables artificial intelligence (AI) applications. In response to NOT-OD-23-082 “Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data,” we propose to supplement the parent project by further enhancing the AI-readiness of multi-omics data for studying cancer pharmacogenomics. Our hypothesis is that biology-guided image embedding of unstructured multi-omics data enhances the information captured by deep learning, enabling accurate modeling and prediction of treatment responses. We aim to achieve three relevant, but independent, goals: 1) methodology: to develop better data conversion methods for AI-readiness of cancer multi-omics, 2) accessibility: to make AI-ready tools and data more accessible to the biomedical research community, and 3) engagement: to promote collaboration on AI-readiness among the communities of bioinformatics, biomedical engineering, and biomedicine. Specifically, Aim 1 will evaluate a comprehensive array of biologically meaningful ways to transform unstructured multi-omics data, including gene mutation and gene expression profiles, to an image-like data format that can be analyzed by convolutional models. Our approach will embed functional similarities of genes to ensure interpretability. In Aim 2, we will develop an interactive web server that provides easy access to data conversion tools and AI-ready cancer data. Finally, in Aim 3, we will organize a community engagement event at a flagship conference of bioinformatics to enhance awareness of current gaps in AI-readiness and foster collaboration and diversity among clinical, basic, and computational scientists and trainees. The proposed supplement has brought together a collaborative team of experts from diverse disciplines, covering cancer bioinformatics, genomics and pharmacogenomics, AI methodology, and community engagement events. Successful completion of this supplement will have a significant impact on advancing the AI-readiness of large cancer data, aligning with the objectives of the parent R00 project.

Narrative Understanding the mechanisms by which drugs operate against pediatric cancers is challenging. We propose to use artificial intelligence (AI) to comprehensively integrate available data sets and create a predictive model for drug responses in cancer cells. In this supplement, we aim to improve the organization of complex genomic data to enhance the comprehension by AI, make tools and data more accessible to other scientists, and facilitate collaboration by bringing together researchers from diverse fields.