预测单基原料扩展效率的模型,从原料模板双重复合中的单次不匹配的位置和类型来预测
Myong-Rim Ri1, Jin-Sok Kang1, Myong-Ryong Ri1
1Department of Life Science, University of Science, Pyongyang, Democratic People's Republic of Korea.
Heliyon
|August 14, 2023
概括
这项研究开发了多重线性回归 (MLR) 和人工神经网络 (ANN) 模型,以预测初始模板不匹配如何影响聚合酶链反应 (PCR) 延伸效率. 该ANN模型在预测原料扩展效率方面表现出高准确性,有助于原料设计和特异性测试.
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.
- 计算生物学 计算生物学
背景情况:
- 原料模板不匹配 (MMs) 显著影响聚合酶链反应 (PCR) 放大和特异性.
- 了解不匹配位置和类型对原料延伸的影响对于优化PCR试验至关重要.
研究的目的:
- 开发和比较多重线性回归 (MLR) 和人工神经网络 (ANN) 模型,以预测在原始模板不匹配的情况下的原始扩展效率.
- 评估这些模型在预测单基扩展 (SBE) 效率方面的准确性.
主要方法:
- 开发了使用变量表示不匹配位置,完美匹配位置和吉布斯自由能量变化的MLR模型.
- 构建ANN模型,在输入,隐藏和输出层中具有不同数量的神经元.
- 在训练,测试和所有数据上使用相关系数 (R) 评估模型性能.
主要成果:
- 两种MLR和ANN模型都超过了先前的多项式回归模型,用于预测单MM原始体的SBE效率.
- 采用不匹配位置,类型和回火温度的ANN模型6实现了高预测准确度 (R值为0.9870,0.9782和0.9857).
结论:
- ANN模型,特别是ANN模型6,提供了一个非常准确的方法来预测由不匹配影响的原料扩展效率.
- 这些预测模型显示了改善原料设计和增强基因组数据库中的原料特异性评估的重大前景.
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