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Updated: Feb 13, 2026

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深层囊神经网络用于识别使用序列到图像转换的基于本地嵌入特征的抗癌
Shahid Akbar1,2, Ali Raza3,4,5, Matee Ullah6,5
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.
BMC biology
|February 12, 2026
概括
一个新的模型,pACP-CapsNet,准确地识别了97.0%的抗癌 (ACP). 这种计算工具为癌症药物开发提供了一个有希望的,低副作用的替代方案.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 癌症仍然是一个重大的全球健康挑战.
- 传统的癌症治疗由于高成本和副作用而面临限制.
- 抗癌 (ACP) 为癌症治疗提供了一个有希望的替代方案.
研究的目的:
- 开发一个有效的计算模型,以准确识别ACP.
- 利用深度学习来增强对抗癌序列的预测.
- 为了解决现有非洲和非洲国家识别方法的局限性.
主要方法:
- 用SMR和RECM将输入序列转换为图像.
- 特征提取涉及HOG,DWT和CLBP转换,创建混合特征空间.
- 混合青跳跃算法 (SFLA) 用于特征选择.
- 囊神经网络 (CapsNet) 用于分类.
主要成果:
- 在培训数据上,pACP-CapsNet模型实现了97.0%的准确性和0.98 AUC.
- 该模型在ACP240和ACP740测试组上表现出优于现有方法的性能.
- 集成功能和SFLA选择的功能改善了预测率.
结论:
- 该pACP-CapsNet模型显示了高效率和稳定性,用于ACP的识别.
- 这种工具在学术研究和癌症药物设计中具有潜在的应用.
- 该模型有助于药物诊断和开发新型癌症疗法.
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