卡迪-T:通过变压器解释癌症词典
Jamey O'Neill1,2, Gudur Ashrith Reddy1,2, Nermeeta Dhillon1
1Mechanical Engineering Department, San Diego State University, San Diego, CA, USA.
medRxiv : the preprint server for health sciences
|August 26, 2024
概括
一个新的框架,通过变压器检测致癌物 (CarD-T),利用人工智能有效地识别科学文献中的潜在致癌物. 这种自动化方法有助于癌症流行病学和公共卫生,因为它比手工方法更快地分析大量数据.
科学领域:
- 计算毒理学计算毒理学
- 癌症流行病学癌症流行病学
- 生物信息学是一种生物信息学.
背景情况:
- 准确的致癌物鉴定对于癌症流行病学至关重要.
- 随着生物医学文献的数量不断增加,现有的方法面临着挑战.
- 手动审查科学文本是耗时的,容易造成差异.
研究的目的:
- 引入通过变压器检测致癌物 (CarD-T) 框架,用于自动指定致癌物.
- 提高科学文本中识别潜在致癌物质的效率和准确性.
- 为毒理学研究提供可扩展的解决方案.
主要方法:
- 开发了通过变压器检测致癌物 (CarD-T) 框架,集成基于变压器的机器学习和概率统计分析.
- 使用命名实体识别 (NER) 在PubMed摘要和上下文分类器上接受了培训.
- 分析了25年的期刊出版数据,根据致癌性和致癌性MeSH术语进行索引.
- 采用贝叶斯时间概率致癌称号 (PCarD) 来分析有争议的证据.
主要成果:
- 根据测试数据,CarD-T准确地确定了所有已确定的IARC组1和2A致癌物.
- 提名了大约1500种额外的潜在致癌物质,并提供了支持性出版物.
- 与GPT-4相比,实现了高回忆率 (0.857) 和F1得分 (0.875) 的高回忆率.
- 突出554个具有争议性致癌证据的实体,PCarD进一步分析了这些实体.
结论:
- CarD-T框架是一个强大的,高效的,可扩展的工具,用于识别生物医学文献中的潜在致癌物.
- 这种人工智能驱动的方法提高了公共卫生对致癌物识别反应的灵活性.
- CarD-T为自动化毒理学调查设定了新的基准,甚至在消费者GPU上也是如此.
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