在piRNA和PIWI类蛋白质研究中的先进人工智能:对反复的神经网络,长期短期记忆和新兴计算技术的系统审查
Jheremy Sebastián Reyes1, Jhonathan David Guevara1, Laura Tatiana Picón1
1Cancer and Molecular Medicine Research Group - CAMMO, Bogotá, D. C., Colombia.
Biomedica : revista del Instituto Nacional de Salud
|December 18, 2025
概括
人工智能,特别是长期短期记忆网络,擅长检测PIWI相互作用RNA用于癌症诊断. 这些先进的模型提高了准确性和灵敏度,为个性化癌症医学铺平了道路.
科学领域:
- 生物分子信息学 生物分子信息学
- 瘤学研究研究
- 人工智能在医学中的应用
背景情况:
- 与PIWI相互作用的RNAs (piRNAs) 是基因调节和癌症中的关键非编码RNA.
- piRNAs正在成为瘤学的关键生物标志物和治疗点.
- 人工智能 (AI) 提供了用于piRNA检测的先进方法.
研究的目的:
- 评估用于piRNA检测的AI模型 (RNN,LSTM,GCN).
- 使用piRNAs评估AI在癌症诊断和预后中的作用.
主要方法:
- 来自主要数据库 (PubMed,Scopus等) 的24项研究的系统审查. ) 的情况.
- 专注于基于AI的piRNA检测在临床/实验环境中.
- 分析的性能指标:准确性,敏感性,特异性.
主要成果:
- 长期短期记忆 (LSTM) 模型实现了最高的准确性 (92.3%) 和灵敏度 (94%).
- 图形卷积网络 (GCNs) 在复杂的piRNA-疾病关联数据集方面表现出色.
- 循环神经网络 (RNN) 和支持矢量机器 (SVM) 也显示出有效性.
结论:
- 人工智能模型,特别是LSTM和GCN,显著改善了用于癌症诊断的piRNA检测.
- 这些人工智能方法支持个性化医学策略.
- 未来的研究应该集中在模型改进和临床整合上.
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