基于多omics数据预测癌症细胞系特定的协同药物组合
Jiaqi Chen1, Huirui Han1, Lingxu Li1
1College of Biomedical Information and Engineering, Kidney Disease Research Institute at the Second Affiliated Hospital, Hainan Engineering Research Center for Health Big Data, Hainan Medical University, Haikou, Hainan, China.
PeerJ
|March 3, 2025
概括
我们开发了XDDC,一种极端梯度增强模型,用于预测用于癌症治疗的协同药物组合. 这种人工智能工具通过识别有效的药物配对,提高治疗效率和降低毒性来增强药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 机器学习在药物发现中的作用
背景情况:
- 组合疗法通过减少剂量和毒性,同时提高疗效,比单药疗法具有优势.
- 预测协同药物组合对于推进癌症治疗策略至关重要.
研究的目的:
- 开发和验证一个先进的机器学习模型,XDDC,用于预测针对癌症细胞系的协同药物组合.
- 利用多样化的生物和化学数据来提高预测准确性和可解释性.
主要方法:
- 利用极端梯度提升 (XGBoost) 算法来构建XDDC模型.
- 综合综合药物特征 (化学结构,ADR,标) 和细胞系特征 (基因表达,甲基化,突变,CNV,RNAi).
- 集成的途径信息将药物和细胞系特征联系起来,以改善预测.
主要成果:
- 在NCI-ALMANAC数据集中,XDDC模型实现了高预测性能,AUC为0.966 ± 0.002和AUPR为0.957 ± 0.002.
- 与其他机器学习方法相比,表现出卓越的性能.
- 通过文献审查和实验证据验证的预测.
结论:
- XDDC提供了一个可解释和准确的平台,用于预测协同作用的药物组合.
- 该模型有效地整合了多omics数据和药物信息,用于药物发现.
- XDDC可以显著帮助临床医生快速选针对特定癌症细胞系的有效药物组合.
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