基于大型语言模型的框架,用于从非结构化数据中自动提取遗传相互作用.
Jaskaran Kaur Gill1, Madhu Chetty1, Suryani Lim1
1Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia.
PloS one
|May 21, 2024
概括
我们开发了GIX,这是一个自动化的框架,用于从文献中提取基因相互作用. GIX与手动策划准确度相匹配,提高了生物发现和研究效率.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 文本挖掘 (Text Mining) 是一个很好的方法.
背景情况:
- 从文献中自动提取生物相互作用对于理解复杂的生物系统和药物开发至关重要.
- 手动策划是黄金标准,但受到越来越多的科学文献体积和复杂性的挑战.
- 现有的文本挖掘工具经常难以识别相关文献,并从复杂的句子结构中准确推断关系.
研究的目的:
- 开发一种自动化和强大的框架,GIX (基因相互作用提取),用于从科学文献中提取生物相互作用.
- 克服手动策划和现有的自动化方法在速度,准确性和处理复杂文本方面的局限性.
- 为基因/蛋白质相互作用提取提供可靠和高效的工具,可以增强现有的生物数据集.
主要方法:
- 使用预训练的大型语言模型,对基因/蛋白质相互作用体 (LLL,RegulonDB) 进行微调.
- 开发了GIX以使用最少的关键词识别相关出版物,并优化句子选择以减少计算负载.
- 实施了第二阶段的关系提取方法,用于评估提取的相互作用的可靠性.
主要成果:
- 通过十倍交叉验证,GIX在基准蛋白质/基因相互作用数据集上实现了与手动策划相当的性能,超过了最先进的方法.
- 在自动化关系提取中证明了增强的稳定性.
- 展示了GIX以新的生物术语和过程来增强数据集的能力,以及其在推断大肠杆菌基因回路中的适用性.
结论:
- GIX为基因相互作用提取提供了一个全自动化的,强大的,准确的解决方案,与手动策划标准相匹配.
- 该框架有效地应对了大规模文献分析的挑战,提高了生物研究的效率.
- GIX有可能加速生物学关系的发现,并支持系统生物学研究.
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