We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes. Our framework stands out for its ability to provide a deeper understanding of gene sets, significantly surpassing GSEA by 40.28% and LLM baselines by 5.38% based on cosine similarity to human annotations. Our analysis further provides insights into future directions of biological processes naming, and implications for bioinformatics and precision medicine.

翻译：我们提出思维图谱作为一种新型框架，用于支持复杂推理，并以基因集分析为例揭示生物过程之间的语义关系。该框架的突出优势在于能够提供对基因集的更深入理解，在与人标注结果的余弦相似度上，比GSEA显著提升40.28%，比大语言模型基线提升5.38%。我们的分析进一步为生物过程命名的未来方向提供了洞见，并对生物信息学和精准医学具有启示意义。