GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases

التفاصيل البيبلوغرافية
العنوان: GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases
المؤلفون: Wang, Zhizheng, Jin, Qiao, Wei, Chih-Hsuan, Tian, Shubo, Lai, Po-Ting, Zhu, Qingqing, Day, Chi-Ping, Ross, Christina, Lu, Zhiyong
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Artificial Intelligence, Computer Science - Computation and Language
الوصف: Gene set knowledge discovery is essential for advancing human functional genomics. Recent studies have shown promising performance by harnessing the power of Large Language Models (LLMs) on this task. Nonetheless, their results are subject to several limitations common in LLMs such as hallucinations. In response, we present GeneAgent, a first-of-its-kind language agent featuring self-verification capability. It autonomously interacts with various biological databases and leverages relevant domain knowledge to improve accuracy and reduce hallucination occurrences. Benchmarking on 1,106 gene sets from different sources, GeneAgent consistently outperforms standard GPT-4 by a significant margin. Moreover, a detailed manual review confirms the effectiveness of the self-verification module in minimizing hallucinations and generating more reliable analytical narratives. To demonstrate its practical utility, we apply GeneAgent to seven novel gene sets derived from mouse B2905 melanoma cell lines, with expert evaluations showing that GeneAgent offers novel insights into gene functions and subsequently expedites knowledge discovery.
Comment: 30 pages with 10 figures and/or tables
نوع الوثيقة: Working Paper
الوصول الحر: http://arxiv.org/abs/2405.16205Test
رقم الانضمام: edsarx.2405.16205
قاعدة البيانات: arXiv