通过将基因关系与深度学习相结合,改善药物反应预测
Pengyong Li1,2, Zhengxiang Jiang3, Tianxiao Liu1
1School of Computer Science and Technology,Xidian University, 710126 Xi'an, Shaanxi, China.
Briefings in bioinformatics
|April 11, 2024
概括
本研究介绍了深度神经网络整合先前知识 (DIPK),这是一个深度学习框架,用于预测癌症药物反应. 通过整合基因相互作用和表达特征,DIPK提高了准确性,显示了个性化癌症治疗的前景.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 药物基因组学 药物基因组学
背景情况:
- 预测癌症药物反应对于个性化医学至关重要,但由于瘤异质性而受到阻碍.
- 由于复杂的生物数据,现有的方法在准确性和稳定性方面扎.
研究的目的:
- 开发一个深度学习框架 (DIPK) 来准确和可靠地预测癌症药物反应.
- 整合各种生物数据,包括基因相互作用,表达特征和分子拓,使用自我监督的技术.
- 评估DIPK在已知和新细胞系/药物上的性能,以及它对单细胞和临床数据的适用性.
主要方法:
- 开发了深度神经网络集成先前知识 (DIPK),一个深度学习框架.
- 采用自我监督学习来整合基因相互作用网络,基因表达数据和分子拓.
- 与使用癌细胞系数据集的现有方法对DIPK进行了验证,并将其应用于单细胞RNA测序和临床数据.
主要成果:
- DIPK在预测已知和新细胞系和药物的药物反应方面表现优于现有方法.
- 该框架成功地将其应用扩展到单细胞RNA测序数据,用于响应预测和细胞识别.
- 在临床数据中,DIPK准确地预测了病理完整响应 (pCR) 组与残留疾病组相比,在病理完整响应 (pCR) 组中更高的帕克利塔塞尔反应.
结论:
- 整合基因相互作用关系显著提高了药物反应预测的准确性.
- 通过增强预测能力,DIPK为个性化癌症治疗提供了强大的多功能工具.
- DIPK有可能帮助个人化癌症治疗策略的临床决策.
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