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DEEP-EP:通过整体残留卷积神经网络识别表观遗传蛋白质,用于药物发现

Farman Ali¹, Abdullah Almuhaimeed², Majdi Khalid³

¹Department of Computer Science, Bahria University Islamabad Campus, Pakistan.

Methods (San Diego, Calif.)

|April 15, 2024

概括

这项研究引入了一种新的深度学习模型,以准确预测表观遗传蛋白 (EP). 集成残余卷积神经网络 (ERCNN) 与二偏离预期平均值 (DDE) 编码显示出卓越的性能,加速药物发现.

科学领域:

生物化学生物化学
计算生物学计算生物学
遗传学遗传学是一种遗传学.

背景情况:

表观遗传蛋白 (EP) 与各种疾病有关,包括癌症和神经系统疾病.
了解EP功能对于确定治疗和药理目标至关重要.

研究的目的:

开发一种基于深度学习的新型模型,用于准确预测表观遗传蛋白 (EP).
评估不同的蛋白质序列表示方法和深度学习算法用于EP预测.

主要方法:

为模型培训和评估生成了两个不同的数据集.
采用了三种蛋白质序列编码策略:从预期平均值 (DDE) 偏离二,二组成 (DPC) 和组氨基酸 (GAAC).
训练并比较了四种深度学习模型:集成残余卷积神经网络 (ERCNN),生成对抗网络 (GAN),卷积神经网络 (CNN) 和门式循环单元 (GRU).

主要成果:

结合DDE编码和ERCNN模型,在两个数据集上实现了最高的预测性能.
对比分析强调了ERCNN对GAN,CNN和GRU的有效性.

结论:

深度学习模型,特别是ERCNN与DDE,显示出准确的EP预测的巨大潜力.
这种方法可以加速对EP功能的研究,并简化与疾病相关的表观遗传标的药物发现过程.

关键词:

深度学习是一种深度学习.与预期平均值的二基偏差.整合剩余卷积神经网络.

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