超级ACP:通过可扩展的特征提取和基于邻居的自适应合成来进行抗癌类别的前沿混合框架
Bangyi Zhang1, Yun Zuo1, Jun Wan1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
PLoS computational biology
|September 11, 2025
概括
超ACP增强了使用深度学习和自适应采样来识别抗癌 (ACP). 这种强大的框架提高了预测的准确性,并为癌症治疗提供了关于结构功能关系的见解.
科学领域:
- 生物化学 生物化学
- 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:
- 癌症是全球主要的死亡原因,需要新的治疗策略.
- 由于有针对性的作用和低毒性,抗癌 (ACP) 是有前途的.
- 现有的非洲国家和地区识别方法面临着复杂的特征和数据不平衡的问题.
研究的目的:
- 开发一个先进的框架,HyperACP,用于准确和可解释的抗癌识别.
- 克服传统方法在捕获深度序列特征和处理类失衡方面的局限性.
主要方法:
- 利用ESMC蛋白语言模型进行全面的序列特征提取.
- 实现了自适应ANBS算法,以解决决策边界的严重阶级失衡问题.
- 集成的SHAP-Res用于机械解释性,识别关键的残留物贡献.
主要成果:
- 在各种数据集和验证策略中,HyperACP显著超过了最先进的方法.
- 实现了高性能指标,包括精度,灵敏度,特异性,MCC和AUC.
- 确定了关键残留物 (K,L,F,G),对抗癌活性至关重要,提供了生物学见解.
结论:
- HyperACP为抗癌的识别提供了一个强大的和可解释的工具.
- 该框架为非洲和非洲国家/地区的结构功能关系提供了新的见解.
- 这项研究推进了用于开发下一代癌症治疗方法的计算方法.
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