文章：

抗癌药物筛选主动学习策略的全面研究

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

原文发布日期：26 January 2024

DOI: 10.3390/cancers16030530

类型: Article

开放获取: 是

英文摘要：

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

摘要翻译： 

众所周知，相同组织学类型的癌症对治疗的反应可能存在差异。因此，计算药物反应预测在临床前药物筛选研究和临床治疗方案设计中具有至关重要的意义。构建药物反应预测模型需要通过筛选实验生成治疗反应数据，并将其作为训练预测模型的输入。本研究探讨了多种主动学习策略，旨在通过选择实验生成反应数据，以实现以下两个目标：(1) 提升基于这些数据构建的药物反应预测模型的性能；(2) 识别有效的治疗方案。本研究聚焦于构建针对癌细胞系的药物特异性反应预测模型。我们设计并应用了多种方法筛选细胞系，包括随机法、贪婪法、不确定性法、多样性法、贪婪与不确定性结合法、基于采样的混合法以及基于迭代的混合法。所有方法均通过以下两个标准进行评估与比较：(1) 经实验验证为有效的阳性结果数量；(2) 基于选定实验数据训练的反应预测模型的性能。通过对57种药物进行分析，结果显示相较于随机和贪婪采样方法，主动学习方法在识别阳性结果方面具有显著提升。与贪婪采样方法相比，主动学习方法在部分药物和分析过程中也显示出反应预测性能的改进。

原文链接：

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening