药理学广泛的抗癌的有效选,使用来自Transformers战略的先进的双向编码器表示
Yupeng Niu1,2, Zhenghao Li1,2, Ziao Chen3,2
1College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.
Heliyon
|May 20, 2024
概括
这项研究引入了一种新的深度学习模型,用于识别抗癌 (ACP),显著提高查效率. 先进的BERT模型加速了针对精密瘤学的瘤治疗药物的发现.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 传统的抗癌 (ACP) 鉴定是耗时和昂贵的,阻碍了精确的瘤学.
- 需要有效和准确的方法来发现针对癌症治疗的新型ACP.
研究的目的:
- 开发一种基于深度学习的查方法,用于识别抗癌 (ACP).
- 提高对瘤治疗药物的ACP发现的效率和准确性.
主要方法:
- 自然语言处理 (NLP) 和伪氨基酸组合 (PseAAC) 的整合用于ACP属性提取.
- 开发和优化一个BERT模型用于ACP检测,超越现有方法.
主要成果:
- 优化的BERT模型在ACP检测中表现出卓越的准确性和选择性.
- 实现了高性能指标:0.9726 AUC和0.9385 F1评分 (5倍CV),以及0.9848 AUC和0.9371 F1评分 (外部验证).
结论:
- 建议的深度学习方法大大减少了在非洲和非洲国家研究中的时间和财务负担.
- 这种方法加速了ACP的发现和临床应用,推进了精确瘤学.
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