Explainable Reasoning Path Inference of Anti-Cancer Drug Sensitivity on Genomic Knowledge Graph via Macro-Micro Agent Collaborative Reinforcement Learning

Jianing Xi

Abstract:

Artificial intelligence (AI) based anticancer drug recommendation
systems have emerged as powerful tools for precision
dosing. Although existing methods have advanced in terms
of predictive accuracy, they encounter three significant obstacles,
including the “black-box” problem resulting in unexplainable reasoning,
the computational difficulty for graph-based structures,
and the combinatorial explosion during multi-step reasoning. To
tackle these issues, we introduce a novel Macro-Micro agent Drug
sensitivity inference (MarMirDrug). Specifically, our methodology
enhances interpretability via knowledge graphs (KG). To reduce
computational overhead, our MarMirDrug also transforms the
graph structures ofKGinto low-dimensional embeddings. To manage
the combinatorial explosion of graph paths that occurs with
increasing inference steps, we incorporate a macro-micro dualagent
reinforcement learning algorithm, and combines reinforcement
learning with KG-based reasoning to infer path reasoning.
In computational experimental outcomes, the efficiency of our
embedding model surpasses that of the baselinemodel, achieving an
average improvement of 117.65% for hit score. Also, our dual-agent
framework exhibits superior performance and interpretability in
drug response prediction, with AUC values outperforming conventional
baseline methods. In summary, our approach integrates
interpretability, computational efficiency, and predictive accuracy,
providing novel contributions to precision medicine.