通过对比学习增强药物和细胞系表示以改进抗癌药物优先级排序
Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization
AI总结：
本文提出用对比学习保留药物作用机制和细胞系癌症类型的关系结构，以改进药物和细胞系表示，从而提升抗癌药物优先级排序的性能和个性化程度。
摘要：
由于癌症的复杂性质和对治疗的可变反应，以组学序列分析为依据的精准肿瘤学已成为当前的标准治疗方式。然而，每位患者产生的数据量使得快速确定最佳治疗方案变得困难。此外，有限的数据可用性阻碍了计算方法学习与有效药物-细胞系配对相关模式的能力。在这项工作中，我们提出使用对比学习来改进学习到的药物和细胞系表示，通过保留与药物作用机制和细胞系癌症类型相关的关系结构。除了相对于最先进方法实现增强的性能外，我们发现使用我们学习到的表示的分类器在进行预测时表现出对药物来源特征和细胞系来源特征更加均衡的依赖。这有助于实现更加个性化的药物优先级排序，该排序由与药物耐药性相关的信号所指导。
英文摘要：
Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patients makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods' abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanism of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balances reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
