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一个omics驱动的计算模型用于血管蛋白预测:通过Ens-deep-AGP推进治疗策略

Naif Almusallam¹, Farman Ali², Atef Masmoudi³

¹Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

International journal of biological macromolecules

|October 18, 2024

概括

一个新的计算模型,Ens-Deep-AGP,准确地预测血管生成蛋白 (AGPs),对于理解癌症等疾病至关重要. 这种方法加速了新治疗点的发现和药物开发.

科学领域:

生物化学和生物信息学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

血管蛋白 (AGPs) 在生理和病理过程中至关重要,包括癌症和心血管疾病.
目前用于识别AGP的方法效率低下,需要先进的预测工具.
AGP代表了各种人类疾病的重要治疗点.

研究的目的:

开发一种新的计算模型,Ens-Deep-AGP,用于准确预测血管性蛋白质.
为全面的蛋白质序列分析引入创新的特征工程技术.
提高AGP识别治疗应用的效率和准确性.

主要方法:

开发了Ens-Deep-AGP,这是一个集成新功能工程方法的计算模型.
使用定位特定得分矩阵-分解-离散等边变换 (PSSM-DC-DCT) 和定位特定得分矩阵-自动交叉离散波形变换 (PSSM-ACC-DWT) 进行特征提取.
采用多头集团残余卷积神经网络 (MERCNN) 架构进行蛋白质分类.

主要成果:

Ens-Deep-AGP实现了高预测准确度:在培训数据集上达到99.79%,在测试数据集上达到92.97%.
该模型的性能优于现有的方法,如卷积神经网络 (CNN),门式循环单元 (GRU) 和双向长期短期记忆网络 (BiLSTM).
在AGP识别中证明了拟议的功能工程和深度学习架构在AGP识别中的有效性.

结论:

Ens-Deep-AGP提供了一个高度准确和高效的计算方法来预测血管性蛋白质.
该研究强调了先进生物信息学工具在加速药物开发和确定新疗法目标方面的潜力.
准确的AGP预测对于推进癌症,心脏病和中风治疗研究至关重要.

关键词:

血管性蛋白质是一种血管性蛋白质.深度学习是一种深度学习.位置特定的评分矩阵.

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