CircCNNs,一个卷积神经网络框架,以更好地了解外来circRNAs的生物发生
1Department of Biology, Miami University, Oxford, OH, 45056, USA. wangc90@miamioh.edu.
Scientific reports
|August 16, 2024
概括
机器学习模型揭示了通过反向拼接 (BS) 推动循环RNA (circRNA) 生物发生的动机. 这种方法提高了circRNA检测的准确性和特异性,这对于癌症生物标志物开发至关重要.
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:
- 循环RNAs (circRNAs) 越来越多地被认为是潜在的癌症生物标志物.
- 循环RNA生物发生的精确机制,特别是背接 (BS),仍然不完全理解.
- 现有的用于circRNA检测的计算工具经常遭受高假阳性率.
研究的目的:
- 通过机器学习研究循环RNA (circRNA) 形成中背部拼接 (BS) 背后的分子机制.
- 开发和优化计算模型,以准确识别BS事件.
- 为了提高circRNA检测的特异性和准确性,用于潜在的生物标记物应用.
主要方法:
- 策划了一组高质量的数据集,用严格的过方法对背拼接 (BS) 和线性拼接 (LS) 的前子对进行了严格的过.
- 开发并比较了两个卷积神经网络 (CNN) 基础模型,用于分析拼接连接序列和提取图案.
- 实施了一种新的数值方法来计算反向互补匹配 (RCMs),并将其集成到CircCNNs框架中.
主要成果:
- 确定了与已知BS相关基因 (MBNL1,QKI,ESPR2) 相关的特定动机.
- 通过基本的CNN模型实现了高特异性 (超过90%),显著减少了假阳性.
- 结合RCM的CircCNNs框架进一步提高了性能,达到88%的预测准确度.
结论:
- 机器学习,特别是CNN,可以有效地阐明circRNA生物发生的机制.
- 开发的CircCNNs框架为识别circRNA形成事件提供了更准确和更具体的方法.
- 这一进步有望提高circRNAs作为癌症检测生物标志物的可靠性.
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