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EVMP:通过扩展视觉突变者优先框架增强机器学习模型,用于合成促进者力量预测

Weiqin Yang^1,2, Dexin Li^1,2, Ranran Huang¹

¹Institute of Marine Science and Technology, Shandong University, Qingdao, China.

Frontiers in microbiology

|July 21, 2023

概括

我们开发了EVMP (扩展视觉突变优先),这是一个新的框架,通过利用突变信息更准确地预测合成促进剂的强度. EVMP显著提高了机器学习模型的性能,推进了合成生物学应用.

科学领域:

合成生物学合成生物学
代谢工程是代谢工程.
计算生物学是一种计算生物学.

背景情况:

准确预测合成促进剂强度对于代谢工程和合成生物学至关重要.
对促进剂强度的实验性注释是耗时和费力的.
现有的机器学习模型用于合成促进体强度预测,由于合成促进体的接近而受到限制.

研究的目的:

为了增强机器学习模型用于合成促进器强度预测.
提出一个通用框架,EVMP (扩展视野突变优先),更有效地利用突变信息.

主要方法:

EVMP将合成促进剂转化为基促进剂和k-mer突变.
这些组件由BaseEncoder和VarEncoder分别处理.
可选的数据增强生成多个数据副本使用不同的基础促进器.

主要成果:

EVMP在Trc合成促进子库中增强了ML模型的性能,提高了高达61.30%的平均绝对误差 (MAE).
EVMP 提高了 15.25% (MAE) 和 4.03% (R2) 的最先进记录.
与非EVMP最先进的数据相比,数据增强进一步提高了17.95% (MAE) 和7.25% (R2) 的性能.

结论:

扩展视力 (k-mer) 对于 EVMP 的有效性至关重要.
EVMP减轻了过度平滑现象,有助于其预测能力.
EVMP突出突变信息,显著提高合成促进剂强度预测的准确性.

关键词:

欧洲人民议会 (EVMP)深度学习是一种深度学习.机器学习是机器学习.促销者力量预测预测合成促进子突变库合成促进子突变库

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