CEREP: A Graph-Constrained Explainable Reasoning Engine for Multi-Omics Precision Oncology

Abstract: This paper presents the Computational Explainable Reasoning Engine for Precision Oncology (CEREP) for targeted cancer therapeutics. The proposed system integrates deterministic bioinformatics pipelines with structured biological knowledge graphs to optimize algorithmic interpretability, balancing multi-omics integration, causal pathway modelling, hallucination elimination, and clinical auditability. A graph-constrained decoding algorithm processes high- dimensional patient profiles to adaptively regulate Large Language Model (LLM) token generation under strict biological constraints. The framework consists of five synergistic modules: (i) a multi-omics processing layer utilizing nf-core/sarek and quantms for deterministic variant annotation and protein quantification , (ii) a Biolink-compliant knowledge graph constructed via BioCypher for mapping validated biological relationships , (iii) a KG-Trie structural index bounding the LLM search space to established biochemical realities , (iv) a lightweight KG-specialized LLM for constrained multi- hop path extraction, and (v) a Fusion-in-Decoder (FiD) module utilizing a general LLM for inductive clinical narrative synthesis. Experimental evaluation on TCGA and CPTAC breast cancer (BRCA) datasets demonstrates substantial improvements over conventional Retrieval-Augmented Generation (RAG) paradigms, achieving 100% traceable explanation chains, zero biologically invalid reasoning sequences, and highly efficient graph traversal in constant time. The integrated graph-constrained reasoning module achieves state-of-the-art accuracy with zero reasoning hallucination, significantly reducing latency and exhibiting strong zero-shot generalizability to unseen knowledge graphs. Implementation on a FastAPI-Next.js platform with interactive React Flow visualizations ensures transparent, clinical- grade operation. This hybrid framework, combining symbolic graph-theoretic guardrails with vast parametric intelligence, represents a paradigm shift toward intelligent, fully auditable AI systems for next-generation precision oncology.

Keywords: Explainable Artificial Intelligence (XAI), Precision Oncology, Multi-Omics Integration, Graph-Constrained Reasoning, Biological Knowledge Graphs, Large Language Models (LLMs), Proteogenomics, Deterministic Bioinformatics, Mechanistic Explanations, Clinical Decision Support