深度AGP:通过整合二维卷积神经网络与离散的共弦变换来预测血管蛋白
Farman Ali1, Wajdi Alghamdi2, Alaa Omran Almagrabi3
1Sarhad University of Science and Information Technology Peshawar, Mardan Campus, Pakistan.
International journal of biological macromolecules
|June 10, 2023
概括
研究人员开发了Deep-AGP,这是一种深度学习模型,用于识别血管性蛋白质 (AGPs). 这种计算方法有助于理解和开发癌症和神经退行性疾病的治疗方法.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 血管生成蛋白 (AGPs) 对于新的血管形成至关重要.
- AGP在癌症,心血管和神经退行性疾病中起着重要作用.
- 准确识别AGP对于诊断和治疗至关重要.
研究的目的:
- 建立一种基于深度学习的新型计算模型,用于识别血管性蛋白质.
- 探索和增强用于AGP预测的特征提取方法.
- 用严格的交叉验证技术验证模型的性能.
主要方法:
- 构建一个基于序列的数据集用于AGP识别.
- 开发了一种新型特征编码器:位置特定的评分矩阵-分解-离散的等号变换 (PSSM-DC-DCT).
- 利用现有的描述符 (DDE,Bi-PSSM) 并将特征输入到2D-CNN和机器学习分类器中,通过十倍交叉验证进行验证.
主要成果:
- 拟议的PSSM-DC-DCT特征描述器与2D-CNN相结合,实现了最高的预测准确性.
- 深度AGP模型在训练和测试数据集上都表现出卓越的性能.
- 该模型被证明是识别血管性蛋白质的准确预测器.
结论:
- 开发的Deep-AGP模型为识别血管生成蛋白提供了一个强大的工具.
- 这种方法有可能促进癌症,心血管和神经退行性疾病的研究.
- 深度AGP可能会促进针对相关疾病的新疗法策略和药物设计.
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