协会过和生成对抗网络用于预测 lncRNA相关疾病
Hua Zhong1, Jing Luo2, Lin Tang3
1School of Information Science, Yunnan Normal University, Kunming, China.
BMC bioinformatics
|June 5, 2023
概括
本研究介绍了LDAF_GAN,这是一种使用生成对抗网络预测长非编码RNA (lncRNA) -疾病关联的新方法. 该模型在识别潜在的lncRNA-疾病联系方面表现出高准确性,有助于疾病发病研究.
科学领域:
- 基因组学就是基因组学.
- 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:
- 长非编码RNA (lncRNAs) 在生物过程和疾病发展中起着至关重要的作用.
- 准确预测lncRNA与疾病的关联对于理解疾病的发病因子和改善诊断至关重要.
研究的目的:
- 开发和评估一种新的方法,LDAF_GAN,用于预测长非编码RNA与疾病之间的关联.
- 利用生成对抗网络 (GAN) 与关联过相结合,以提高预测准确度.
主要方法:
- LDAF_GAN方法使用生成器和区分器,结合关联过和负采样.
- 关联过精细化了发电机输出,以专注于相关的 lncRNA-疾病对.
- 负采样和损失函数中的正数项改善了模型训练,并防止了歧视者逃避.
主要成果:
- 在五倍交叉验证中,LDAF_GAN在两个数据集上取得了优异的性能,AUC值为0.9265和0.9278.
- 案例研究显示,已知 lncRNAs 的预测准确度很高,其中前十名的预测率在某些情况下达到100%.
- 该模型有效地预测了现有的和新的 lncRNA 的关联.
结论:
- LDAF_GAN 显示了在预测 lncRNA 与疾病的关联方面有很大的潜力.
- 该模型在交叉验证和案例研究中的表现凸显了它对未来研究的预测能力.
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