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一个用于评估深度学习模型的自动化框架,用于拼接地点预测

Amin Zabardast¹, Elif Güney Tamer¹, Yeşim Aydın Son¹

¹Department of Health Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.

Scientific reports

|June 23, 2023

概括

一个新的框架自动化了深度学习模型评估用于RNA拼接位置预测,简化了在长短期记忆 (LSTM) 网络上选择最佳卷积神经网络 (CNN) 模型的选择.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

RNA拼接对于mRNA处理和疾病关联至关重要.
准确的拼接部位预测对于基因发现和疾病变体识别至关重要.
深度学习模型在基因组信号分类中显示出高准确度.

研究的目的:

为基于深度学习的拼接部位检测器进行自动评估提供一个新的框架.
消除用不同的模型和配置进行耗时的手动实验.
为了促进对RNA拼接部位预测任务的最佳模型的选择.

主要方法:

开发了一个用于深度学习模型自动评估的框架.
创建了五个不同级别的网络系列的蓝图.
在两个数据集上比较了卷积神经网络 (CNN) 和双向长期短期记忆 (BLSTM) 模型.

主要成果:

在人类和C. elegans接部位预测方面,CNN模型的表现优于BLSTM和双向接重复单元 (BGRU) 模型.
与BLSTM相比,CNN获得了更高的准确性,F1得分和AUC-PR.
美国有线电视新闻网 (CNN) 展示了更快的学习和卓越的序列模式提取能力.

结论:

拟议的框架可实现深度学习模型的自动化,高效的选择,用于拼接位置分析.
对于RNA拼接地点预测,CNN是一个比BLSTM和BGRU更有效的深度学习架构.
这一框架可以帮助研究人员为各种基因组分类任务选择最佳模型.

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