Abstract A019: A fully transparent and automatable form of AI for biomarker and new target discovery using diverse multi-omics data

Abstract Modern chemical biology and multi-omics approaches capture far more data than can be digested in human readable form, creating an opportunity for machine-based methods 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 combining 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 have 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 agentic large language models (LLMs) as part of retrieval-augmented generation (RAG) strategies to build intelligent, autonomous workflows for discovery of new targets, biomarkers, and combination treatment strategies. Importantly, unlike ML methods, our focal graph approach preserves the provenance of the underlying experimental data, allowing the processes and conclusions to be examined and evaluated in tremendous detail by human or machine-based methods. Using focal graphs combined with agentic LLMs and workflow automation, we can plan and execute research programs that lead to the discovery of entirely novel biological insights, including new potential biomarkers, original drug targets, drug repurposing strategies, and combination therapy hypotheses. Here we present the theoretical underpinnings of focal graphs and their automation, as well as sharing selected findings related to novel disease therapeutic and biomarker strategies. These results derive their support from multiple, diverse, large-scale experimental data sets, with full transparency with respect to the experimental basis of the discoveries. As these 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. More information is available at www.plexresearch.com. Citation Format: Jedidiah Gaetz, Timothy R. Wall, Eleni S. Stylianou, Ehab Khalil, Oren Levy, Douglas W. Selinger. A fully transparent and automatable form of AI for biomarker and new target discovery using diverse multi-omics data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A019.

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