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通过神经学习提高抗癌药物选择和优先级,以排名
Vishal Dey1, Xia Ning1,2,3
1Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, United States.
Journal of chemical information and modeling
|May 13, 2024
概括
新的神经排名模型通过优先考虑有效药物来改善个性化癌症治疗. 这些数据驱动的方法在识别特定癌症细胞系的最佳候选药物方面优于现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 个性化癌症治疗需要了解药物与癌症细胞系相互作用.
- 高通量查产生了用于计算药物反应建模的大数据集.
- 对于单个细胞系来说,准确的药物优先级仍然是一个挑战.
研究的目的:
- 开发先进的神经排名方法,以改善癌症治疗中的药物优先级.
- 解决回归和分类在预测药物疗效方面的局限性.
- 用大规模的药物反应数据来制定药物选择作为排名问题.
主要方法:
- 开发了新的对对和列表神经排名模型 (Pair-PushC,List-One,List-All).
- 在不同癌症细胞系中利用大规模的药物反应数据.
- 学习药物和细胞系的潜伏表征,以评分药物的有效性.
主要成果:
- 拟议的排名方法表现优于最先进的基线,提高了药物选择率高达25.6% (hit@20).
- 学习的潜空间揭示了信息聚类和捕获的生物特征.
- 综合评估提供了各种计算方法的客观比较.
结论:
- 神经排名方法为个性化癌症药物选择提供了强大的数据驱动策略.
- 开发的方法提高了识别有效抗癌药物的准确性和效率.
- 未来的研究可以利用这些模型来改善瘤学中的临床决策.
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