Abstract B043: A fully transparent and automatable form of AI for multi-omics precision medicine

Abstract Acquired drug resistance and heterogeneity of patient responses to cancer therapeutics represent major challenges to delivering effective treatments to patients. Predictive biomarkers provide valuable information for increasing drug responses among treatment cohorts by guiding the delivery of drugs to patients most likely to benefit. However, the identification of biomarkers of drug sensitivity and resistance remains challenging, and their mechanistic basis is difficult to discern. Large-scale multi-omics methods represent a promising approach; however, they capture far more data than can be digested in human readable form, creating an opportunity for machine-based technologies to significantly expand the boundaries of scientific knowledge. Artificial intelligence (AI) methods promise to fill this gap, however significant challenges remain to adapt machine learning (ML) based approaches to data that is noisy, complex, and bespoke while still preserving the transparency required for scientific and regulatory decision making. Herein we describe a novel approach, which combines knowledge graphs and centrality algorithms, allowing the construction of systems that are scalable, robust to noise, concise, and perhaps most importantly, highly transparent. The central construct, which we’ve termed a “focal graph”, can seamlessly integrate information across multiple, diverse, complex data sets, including large-scale chemical biology and multi-omics data. Focal graphs can be combined with large language models (LLMs) as part of a retrieval-augmented generation (RAG) strategy to build intelligent, autonomous drug discovery workflows that preserve the provenance of the underlying experimental data, and whose processes and conclusions can be examined and evaluated in tremendous detail. Focal graphs can be paired with LLMs to autonomously plan and execute research programs, potentially resulting in discoveries that are entirely novel, and derive their support from multiple, diverse, large-scale experimental data sets. As such autonomous systems become more sophisticated and are given access to more data and computational power, they can be expected to generate insights with increasing levels of novelty and experimental support. Here we present the theoretical underpinnings of focal graphs and their automation, as well as selected examples highlighting their application to biomarker development and the investigation of mechanisms underlying the heterogeneity of therapeutic responses in cancer. More information is available at www.plexresearch.com. Citation Format: Douglas W Selinger, Timothy R Wall, Eleni Stylianou, Ehab Khalil, Jedidiah Gaetz, Oren Levy. A fully transparent and automatable form of AI for multi-omics precision medicine [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B043.