LncRNA-顶部:控制的深度学习方法为 lncRNA 基因调节关系注释跨不同的平台
Weidun Xie1, Xingjian Chen1, Zetian Zheng1
1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
iScience
|November 15, 2023
概括
lncRNA-Top使用深度学习预测长非编码RNA (lncRNA) 基因调节. 这个工具可以识别潜在的lncRNA目标,有助于理解基因表达调节.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 长非编码RNAs (lncRNAs) 通过与microRNAs (miRNAs) 的相互作用来调节基因表达.
- 预测lncRNA-基因调节关系对于理解基因表达至关重要,但缺乏强大的计算方法.
- 现有的 lncRNA-基因相互作用预测方法是有限的.
研究的目的:
- 开发和介绍IncRNA-Top,这是一款基于深度学习的新型计算工具.
- 准确预测 lncRNA 和基因之间的潜在调节关系.
- 提供对 lncRNA 中介基因调节机制的见解.
主要方法:
- 构建了深度学习模型,使用了12417个lncRNA和16127个基因的数据集.
- 采用严格的方法,包括负采样,随机种子和交叉验证,用于模型培训和评估.
- 使用诸如曲线下的面积 (AUC),精度回忆曲线下的面积 (AUPR) 和precision@k.等指标评估模型性能.
主要成果:
- 在独立数据集上,lncRNA-Top模型表现出强大的预测性能.
- 深入的案例研究显示,在前100个预测的新 lncRNA-基因配对中,有47%被现有文献验证.
- 开发了配套软件,用于注释预测得分与潜在的目标候选人.
结论:
- lncRNA-Top是预测 lncRNA基因调节关系的有效工具.
- 该工具有助于发现新的 lncRNA 标,并促进对 lncRNA 调控功能的理解.
- lncRNA-Top促进了对基因表达和调控网络的研究.
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