一个深度学习模型用于表皮生长因子受体预测,使用整体残余卷积神经网络
Wajdi Alghamdi1, Farman Ali2, Raed Alsini3
1Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Scientific reports
|September 29, 2025
概括
一个新的深度学习模型,ERCNN-EGFR,从氨基酸序列准确地识别了表皮生长因子受体 (EGFR). 这种计算工具为乳腺癌诊断和治疗点发现提供了一种具有成本效益的方法.
科学领域:
- 生物技术是生物技术.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 皮表皮生长因子受体 (EGFR) 过度表达驱动乳腺癌,需要有效的识别方法.
- 目前的方法,如基于动机的方法和免疫组织化学,在准确性,成本和可变性方面都有局限性.
- 准确的EGFR鉴定对于向治疗和个性化乳腺癌治疗至关重要.
研究的目的:
- 开发一种新的深度学习预测器,ERCNN-EGFR,用于直接从初级氨基酸序列中准确识别EGFR.
- 评估和比较各种用于蛋白质特征预测的深度学习框架.
- 为EGFR检测建立一个具有成本效益和可扩展的计算工具.
主要方法:
- 使用组合分布过渡 (CDT),两类伪氨基酸组合 (AmpPseAAC),k间隔联合三描述符 (KSCTD) 和ProtBERT-BFD嵌入的特征提取.
- 通过XGBoost-Feature Forward Selection (XGBoost-FFS) 提升功能精细化,以增强区分能力.
- 评估包括BiLSTM,GRU,GAN和ERCNN在内的深度学习模型,ERCNN表现出卓越的性能.
主要成果:
- ERCNN-EGFR模型实现了高性能指标:93.48%的准确性,94.53%的灵敏性,92.58%的特异性,以及0.816个特征选择后的马修斯相关系数.
- 该模型在独立测试组中表现出强大的性能,达到82.85%的准确性.
- 除研究确定了双余构建块和ProtBERT-BFD特征作为模型预测准确性的关键组件.
结论:
- ERCNN-EGFR提供了一个可扩展,具有成本效益和准确的计算方法来识别EGFR蛋白质.
- 开发的预测器在乳腺癌诊断和个性化医学方面具有重要的潜在应用.
- 这种方法促进了EGFR驱动癌症的高效治疗点发现.
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