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EnsemPred-ACP:将机器和深度学习结合起来,改善抗癌预测

Minjun Kwon¹, Yong Eun Jang¹, Ji Su Hwang¹

¹Department of Molecular Science and Technology, Ajou University, Suwon, 16499, Republic of Korea; Department of Physiology, Ajou University School of Medicine, Suwon, 16499, Republic of Korea.

Computers in biology and medicine

|July 4, 2025

概括

EnsemPred-ACP使用新的机器学习和深度学习组合准确预测抗癌 (ACP). 这种方法通过改善强效治疗的识别来增强癌症治疗的发展.

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:

由于选择性向癌细胞,抗癌 (ACP) 在癌症治疗中表现有前途.
由于复杂的癌症机制,对ACP的准确计算预测具有挑战性.

研究的目的:

引入EnsemPred-ACP,这是一个整体框架,将机器学习 (ML) 和深度学习 (DL) 结合起来,以提高ACP预测.
改进用于癌症治疗的潜在治疗性的识别.

主要方法:

开发了EnsemPred-ACP,这是一个整体框架,集成了ML和DL.
引入了二进制特征 (BPF) 来增强预训练的蛋白质嵌入.
采用双管道架构处理序列特征,嵌入和BPF增强的嵌入.

主要成果:

EnsemPred-ACP实现了高预测性能:准确度为0.863,灵敏度为0.897和特异性为0.830.
该模型表现出强烈的概括性,AUC为0.93.
BPF显著提高了预测准确度,提高了2.5% (ESM2) 和11.1% (ProtT5).

结论:

EnsemPred-ACP有效地识别了潜在的治疗,优于现有的方法.
将BPF与蛋白质嵌入的整合提高了ACP预测的准确性.
这种方法有助于推进基于的癌症治疗方法.

关键词:

抗癌是一种抗癌.二进制配置文件的特征二进制配置文件的特征.生物信息学是一种生物信息学.深度学习是一种深度学习.组合学习学习组合学习机器学习机器学习序列分析是指进行序列分析.

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