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使用深度神经网络和LASSO回归来预测基于mRNA数据的miRNA表达变化.
Franz Leonard Böge1, Helena U Zacharias2, Stefanie C Becker3
1Institute for Animal Genomics, University of Veterinary Medicine Hannover Foundation, Hannover, Germany.
Frontiers in bioinformatics
|July 23, 2025
概括
从mRNA数据中预测microRNA (miRNA) 表达是可行的,使用深度学习和LASSO回归. 这种跨学科的方法对疾病研究有前途,即使在差异表达分析中的变化.
科学领域:
- 疾病研究中的多omics数据分析.
- 计算生物学和生物信息学.
- 基因表达概况. 基因表达概况.
背景情况:
- 了解微RNA (miRNA) 和信使RNA (mRNA) 的相互作用对于疾病分子研究至关重要.
- 配对的miRNA/mRNA表达数据集很少,限制了综合分析.
- 现有的miRNA-目标关系研究依赖于具有实验和计算数据的数据库.
研究的目的:
- 评估从mRNA表达数据中预测miRNA表达特征的可行性.
- 为了证明交叉omics预测的潜力作为一个原则的证明.
- 为了利用公开可用的mRNA数据来推断miRNA表达.
主要方法:
- 利用人工深度神经网络和LASSO回归来预测miRNA的表达.
- 对来自西尼罗河病毒和艾滋病毒感染的七个配对miRNA/mRNA数据集的模型进行了评估.
- 进行数据内部和交叉研究评估,探索数据增强和疾病/非疾病样本的单独模型.
主要成果:
- 在大多数设置中,在个体样本水平上取得了强烈的相关性.
- 在一些数据集中,从微分表达式分析 (DEA) 中观察到日志折变化和p值的相关性.
- 数据增强始终提高了神经网络的性能,对LASSO模型的影响较小.
结论:
- 从mRNA数据中对miRNA表达的交叉omics预测被证明是可行的.
- 这种方法显示出在没有配对数据的情况下推断miRNA配置文件的潜力.
- 差异表达分析中的相关性表明这种方法在疾病研究中的实用性.
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